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The article discusses the optimal types of tasks and exercises that help students to effectively enrich their vocabulary in the Russian language textbook. In addition to the use of traditional types of vocabulary work, innovative ways to increase vocabulary that develop speech and broaden teachers' worldviews are also demonstrated

The use of cooperative learning at the college level was studied by investigating whether different instructor intervention approaches would affect the achievement of college students using cooperative learning methods. Subjects were 97 undergraduate elementary education majors enrolled in three sections of a curriculum course at the University of South Florida (Tampa) College of Education during the second semester of the 1990-91 academic year. Nineteen learning teams were formed and randomly assigned to treatment conditions as follows: (1) directions and materials provided, but no instructor assistance (control group); (2) instructors provided advance organizers; and (3) instructors met with groups after sessions to answer questions and provide assistance. All treatment groups read the same materials and followed a similar schedule of activities. A pretest was followed by a posttest and an attitude questionnaire after the 2-week period. On the posttest, the control and follow-up discussion groups had nearly identical mean scores, with the mean for the advance organizer group more than two points higher. Students liked the cooperative learning situation, believed they learned the materials well, and preferred instructor assistance to the control condition. Results suggest that advance organizers may yield greater learning than do follow-up discussions. Five tables present study findings, and a 17-item list of references is included. (SLD)

The use of cooperative learning at the college level was studied by investigating whether different instructor intervention approaches would affect the achievement of college students using cooperative learning methods. Subjects were 97 undergraduate elementary education majors enrolled in three sections of a curriculum course at the University of South Florida (Tampa) College of Education during the second semester of the 1990-91 academic year. Nineteen learning teams were formed and randomly assigned to treatment conditions as follows: (1) directions and materials provided, but no instructor assistance (control group); (2) instructors provided advance organizers; and (3) instructors met with groups after sessions to answer questions and provide assistance. All treatment groups read the same materials and followed a similar schedule of activities. A pretest was followed by a posttest and an attitude questionnaire after the 2-week period. On the posttest, the control and follow-up discussion groups had nearly identical mean scores, with the mean for the advance organizer group more than two points higher. Students liked the cooperative learning situation, believed they learned the materials well, and preferred instructor assistance to the control condition. Results suggest that advance organizers may yield greater learning than do follow-up discussions. Five tables present study findings, and a 17-item list of references is included. (SLD)

Blended learning can facilitate many opportunities about usage of online discussion for different purposes. As a tool in blended learning, online discussions widen the walls of classroom discussions in a virtual manner. Especially, in teacher education programs case based discussions are very helpful for pre-service teachers to practice real-life situations. By this way, the link between theory and practice can be established. This study aims to examine the pre-service teachers' experiences about three different discussion structures in a case based blended learning environment. This study also explored their thoughts toward the effects of online discussion on their professional development and their suggestions to increase the effectiveness of online discussion. The participants of this study were 14 pre-service teachers from the department of Early Childhood Education (ECE) of a funded university. The six open-ended questions were asked in order to explore the opinions of the pre-service teachers. The qualitative data were analyzed through the matrix displays. (Contains 1 figure.)

This study investigated the effect of using differentiated instruction by integrating multiple intelligences and learning styles on solving problems, achievement in, and attitudes towards math in six graders with learning disabilities in cooperative groups. A total of 60 students identified with LD were invited to participate. The sample was randomly divided into two groups; experimental (n = 30 boys) and control (n = 30 boys). ANCOVA and T.test were employed for data analysis. Findings from this study indicated the effectiveness of differentiated instruction by integrating multiple intelligences and learning styles on solving problems, achievement in, and attitudes towards math in the target students. On the basis of the findings, the study advocated for the effectiveness of using differentiated instruction by integrating multiple intelligences and learning styles on solving problems, achievement in, and attitudes towards math in learning disabled students.

Social loafing research has spanned several decades and fields of study. Research has provided support for both the existence of social loafing and its antecedents within the laboratory, classroom, and work place. Studies regarding the perceptions of social loafing and its effects in the online learning environment, however, are largely non-existent. This study surveyed 227 online learning students who were participating in online learning groups. The study seeks to determine whether the perception of social loafing exists within online learning groups. In addition, several psychosocial factors identified in face-to-face environments are analyzed to determine their impact in online learning groups. Evidence supports both the perception of social loafing in online learning groups as well as similarities between social loafing antecedents in face-to-face groups and those in the online learning environment. (Contains 2 tables.)

Aim: The study compared the pattern of deficits, intelligence and neuropsychological functioning in subcategories of learning disorders. Methods: Forty-six children (16 with reading disorders, 11 with writing disorders and 19 with both reading and writing disorders--mixed group) in the age range of 7-14 years were assessed using the NIMHANS Index of Specific Learning Disabilities, Malin's Intelligence Scale for Indian Children, and the PGI Memory Scale. Results: The mixed group had greater dysfunction than the reading and writing groups in alphabet sequencing and graded division, and the mixed group had greater dysfunction than the writing group in capital letters, division and graded subtraction. Also, the mixed and reading groups had greater dysfunction than the writing group in speech and language. Intellectual functions and mental balance (on PGI memory scale) were more affected in the mixed group in comparison to the writing group. Conclusion: Subtypes of learning disorders differ in terms of their neuropsychological profile with the mixed group having greater dysfunction. (Contains 1 table.)

In response to recent stress on incorporating core thinking into subject curriculum, the mediated learning experience (MLE) is being incorporated into the preservice training modules of the Singapore National Institute of Education (NIE). This study examines: (1) factors contributing to the preservice teacher's commitment to using MLE; (2) specific components of MLE that are actually implemented; (3) the extent to which teachers manifest any transfer when teaching content areas; and (4) practical problems faced by teachers when implementing MLE in a school setting. This study is ongoing and only qualitative data have been analyzed so far. Preservice graduate teachers (n=46) were given 20 hours of training in the application of MLE criteria during their first year at NIE. Graduate teacher trainees were posted for about 10 weeks of teaching practice (practicum) in several primary and secondary schools. Anecdotal records, videotapes of MLE lessons and a reflective rating scale were used. Most participants reported that they would continue to use MLE procedures even after graduation; some suggested that in-service training would facilitate proper implementation of the program. (Contains 10 references.) (LH)

What prompts the students to respond in online dialogic discussion? Why some students chose to fall out? This case study through the lens of phenomenography observation attempts to explain the five decision steps of students to respond in Meta-cognitive Learning in Free Online Groups (MetaL-FrOG) discussion. It presents a part of a research project by the name of Triarchy Perspective on Meta-cognitive Learning in Free Online Groups. The research setting was online learner community on the platform of Free Online Group web intended for post-graduate students enrolled for the paper Psychology of Learning in Faculty of Education, University Malaya, Malaysia. Preliminary study revealed three factors contributed to MetaL-FrOG success: Motivation, Cognitive Resources and Pro-learning Behaviors. This paper only presents a part of the findings under the Pro-Learning Behaviors Sub-theory. We found striking similarities between the model proposed by Latane & Darley (1971), Five Essential Steps to a Pro-social Response in an Emergency, and our research subject. The model which explains the course of a pro-social decision was borrowed and modified as surrogate theory to explain the online discussion response of the students. The insights help educators to better understand what holds students back from fruitful online peer diologic discussion. (Contains 3 figures.)

What prompts the students to respond in online dialogic discussion? Why some students chose to fall out? This case study through the lens of phenomenography observation attempts to explain the five decision steps of students to respond in Meta-cognitive Learning in Free Online Groups (MetaL-FrOG) discussion. It presents a part of a research project by the name of Triarchy Perspective on Meta-cognitive Learning in Free Online Groups. The research setting was online learner community on the platform of Free Online Group web intended for post-graduate students enrolled for the paper Psychology of Learning in Faculty of Education, University Malaya, Malaysia. Preliminary study revealed three factors contributed to MetaL-FrOG success: Motivation, Cognitive Resources and Pro-learning Behaviors. This paper only presents a part of the findings under the Pro-Learning Behaviors Sub-theory. We found striking similarities between the model proposed by Latane & Darley (1971), Five Essential Steps to a Pro-social Response in an Emergency, and our research subject. The model which explains the course of a pro-social decision was borrowed and modified as surrogate theory to explain the online discussion response of the students. The insights help educators to better understand what holds students back from fruitful online peer diologic discussion. (Contains 3 figures.)

What prompts the students to respond in online dialogic discussion? Why some students chose to fall out? This case study through the lens of phenomenography observation attempts to explain the five decision steps of students to respond in Meta-cognitive Learning in Free Online Groups (MetaL-FrOG) discussion. It presents a part of a research project by the name of Triarchy Perspective on Meta-cognitive Learning in Free Online Groups. The research setting was online learner community on the platform of Free Online Group web intended for post-graduate students enrolled for the paper Psychology of Learning in Faculty of Education, University Malaya, Malaysia. Preliminary study revealed three factors contributed to MetaL-FrOG success: Motivation, Cognitive Resources and Pro-learning Behaviors. This paper only presents a part of the findings under the Pro-Learning Behaviors Sub-theory. We found striking similarities between the model proposed by Latane & Darley (1971), Five Essential Steps to a Pro-social Response in an Emergency, and our research subject. The model which explains the course of a pro-social decision was borrowed and modified as surrogate theory to explain the online discussion response of the students. The insights help educators to better understand what holds students back from fruitful online peer diologic discussion. (Contains 3 figures.)

This article describes the teaching of process-oriented counselling skills in a group. The interweaving of theory and practice is discussed. The need for and a method of integrating the personal and professional growth of group members with the experiential and conceptual learning of counselling skills are outlined . The congruence of the content and the educational approach is an important element in the training. The development and significance of the community of learners, an intrinsic dimension of the training, are also described.

Changes in the environment require constant adaptation to optimize future behavior. Learning through reinforcement is essential for adaptation in response to changes in the environment; such ability is thought to play a key role in human cognitive development as well as in the emergence and maintenance of psychiatric disorders, such as ADHD, depression or anxiety. Uncertainty and valence are considered to be two important factors for cognitive adaptation during reinforcement learning (RL). Clinical symptoms have been shown to modulate RL with respect to uncertainty and valence conditions, but little is known how psychopathology affects RL in clinically diagnosed compared to typically developing children and adolescents. In this study, we used a probabilistic learning task in which we experimentally manipulated uncertainty and valence levels. Uncertainty was manipulated by two uncertainty conditions, one which included stochasticity, and another which added volatility. Stochasticity or expected uncertainty refers to learning action-outcome contingencies that were probabilistic and stable (80% contingent and 20% non-contingent feedback). Uncertainty due to the probabilistic outcomes becomes expected once the preferred action-outcome contingency had been learned. Volatility further adds uncertainty to the probabilistic action-outcome contingencies: at change points, the learned action-outcome contingencies have to be reversed. Volatility represents unexpected uncertainty, since the exact points of the change cannot be anticipated. Valence has been examined by effects of both valence condition and prediction error (PE) valence which makes it challenging to compare and generalize results. In our study, our task allowed us to examine both these valence effects. For PE effects, valence is determined by whether the received outcome relative to the expected outcome was positive or negative at each trial (Eckstein, Master, Dahl, Wilbrecht, & Collins, 2022; Rosenbaum, Grassie, & Hartley, 2022). Notably, PE valence represents trial-to-trial valence effects, and learning from a positive PE may lead to a momentary positive surprise that can change quickly from positive to negative, if the next trial comes with a negative PE. For valence as block-wise condition effects, valence is determined by whether the absolute outcome value was positive or negative, such as in reward learning and punishment learning, respectively (Palminteri, Kilford, Coricelli, & Blakemore, 2016). Here, valence may unfold over multiple trials and lead to more enduring valence effects. During reward learning, the preferred outcome was to receive 3 coins compared to 1 coin only; during punishment learning, the loss of 1 coin was preferred than the loss of 3 coins. We also added a third condition of both mixed reward and punishments, which was commonly used in studies that only examined PE valence effects. In this third condition, which we will refer to as mixed condition, the preferred outcome was to receive 1 coin compared to losing 1 coin. Notably, PE valence can be additionally examined in any of the block-wise valence conditions. A pilot study indicated that the task can be applied to both clinical and neurotypical 8-18-year-old children and adolescents, and that their adaptation performance was modulated by valence and uncertainty. Our sample of the main study will include 120 children and adolescents between the age of 8 and 18, one clinical group (n=60) with diagnoses of anxiety, depression or ADHD, and a neurotypical group (n=60). We aim to delineate differences between psychiatric conditions (ADHD, anxiety, depression) for cognitive adaptation in respect to valence and uncertainty. We also aim to explore potential transdiagnostic effects of internalizing and externalizing psychopathology during cognitive adaptation. We will use both behavioral measures, such as accuracy, switching behavior and reaction time, as well as computational modeling measures, with the aim to capture adaptations in latent prediction-related parameters. How one learns in the learning conditions that differ by uncertainty and valence might depend upon the current symptom severity, both in clinical as well as neurotypical groups. Therefore, our aim of this study is to examine individual differences in clinical symptoms and their effects on uncertainty and valence during learning. We will further explore the moderating roles of the individual’s environment, state and trait, such as socioeconomic disparities, motivational traits and momentary affective state. Research Questions (RQ) Effects of Depression Major depressive disorder is an affective disorder whose primary symptoms are low mood and loss of motivation and pleasure in daily life. Despite its primarily affective symptoms, there is robust evidence of cognitive deficits in depression (Gotlib & Joormann, 2010), which may be related to the hypoactivity of serotonin, dopamine and noradrenaline (Eshel & Roiser, 2010; Ruhé, Mason, & Schene, 2007). Of note, depression is considered an internalizing disorder, as opposed to externalizing disorders (Achenbach, 1966; Carver, Johnson, & Timpano, 2017), with a large overlap in symptoms such as with anxiety disorders, specifically within a distress-based or negative affect symptom cluster (Watson, 2005). The overlap in negative affect symptoms between depression and anxiety may contribute to shared differences during RL. Although previous studies have found similarities and differences in the learning effects of depression and anxiety, a direct comparison with a task that manipulates both valence and uncertainty during RL has not been done. DEP-RQ1: What are the effects of depression on uncertainty conditions during RL? Studies that used RL with stable environments, reflecting low uncertainty, found reduced learning performance (Elliott, Sahakian, Herrod, Robbins, & Paykel, 1997; Steele, Meyer, & Ebmeier, 2004). Two studies used computational models to examine learning processes in depression, with in inconsistent results. The studies examined various parameters, including learning rate and reward sensitivity. Learning rate serves as a metric for quantifying how individuals update future values, whereas reward sensitivity reflects an individual's responsiveness to perceived outcomes. Reward learning rates were negatively related to anhedonic depression symptoms (Brown et al., 2021), but unrelated in a meta-analysis (Huys, Pizzagalli, Bogdan, & Dayan, 2013). Reward sensitivity in turn was either reduced (Huys et al., 2013) or increased (Brown et al., 2021). These contrasting findings may partly be explained by the use of a pavlovian and an instrumental learning task, respectively, and our study will focus on instrumental learning. One study reported reduced choice sensitivity in a more depressed student group (Kunisato et al., 2012), suggesting that depression is associated with less value-dependent choice behavior. Because the parameters of reward sensitivity and choice sensitivity are computationally interchangeable, current models cannot answer whether the parameters captured decision-related or feedback-related effects of depression (Browning, Paulus, & Huys, 2022). Additional parameter differences have been reported, such as a more negative valuation of received outcomes during punishment learning in depression (Brown et al., 2021), suggesting valence effects, or a decreased attentional breadth in relation to higher trait rumination (Hitchcock et al., 2022), suggesting impaired attention and memory processes. Further studies are needed to establish robust links between depressive symptoms and computational parameters in low uncertainty learning conditions during RL. Consistent with tasks of relatively low uncertainty, studies with more volatile tasks such as probabilistic reversal learning have reported impaired learning performance in depression (Dombrovski et al., 2010; Dombrovski, Szanto, Clark, Reynolds, & Siegle, 2013; Mukherjee, Filipowicz, Vo, Satterthwaite, & Kable, 2020; Mukherjee, Lee, Kazinka, D Satterthwaite, & Kable, 2020; Must, Horvath, Nemeth, & Janka, 2013; Rupprechter, Stankevicius, Huys, Steele, & Seriès, 2018). Less optimal switching behavior in term of lower win-stay and higher lose-switch behavior (particularly after misleading feedback) was also observed (Dickstein et al., 2010; Dombrovski et al., 2015; Mukherjee, Filipowicz, et al., 2020; Murphy, Michael, Robbins, & Sahakian, 2003; Taylor Tavares et al., 2008). In contrast, only one study also reported no learning differences in relation to depression (Brolsma et al., 2020). Some studies applied computational models, with inconsistent findings, similar to studies with low uncertainty tasks. One study reported reduced learning rates in depressed individuals compared to healthy controls (Mukherjee, Filipowicz, et al., 2020), while another study did not find such differences (Brolsma et al., 2020). Additionally, reduced choice sensitivity was reported (Mukherjee, Filipowicz, et al., 2020; Rupprechter et al., 2018), as well as a decreased memory of observed rewards, putatively due to working memory constraints (Rupprechter et al., 2018). Overall, behavioral findings show a relatively consistent picture of reduced learning performance in depression under both low and high uncertainty learning. However, these studies mostly used tasks of mixed-valence or reward conditions only, and the picture regarding uncertainty under punishment learning is less clear. Further, robust links between computational parameters, depression and uncertainty are yet to be established. There is some evidence that learning rate and choice sensitivity estimates are lower across uncertainty conditions. DEP-RQ2: What are the effects of depression on valence conditions during RL? Past RL studies of depression have usually used either conditions for reward and punishment learning separately, or they used PE valence within a reward task to determine valence effects (i.e. gaining more or less than expected). While both PE valence as well as valence condition (learning from positive or from negative feedback) have been studied extensively in depression, these valence effects may represent distinct characteristics on learning which need to be disentangled. To shed light on the relevance of the way valence is manipulated in relation to depression, our study includes 3 valence conditions: reward learning, punishment learning and mixed valence learning. Of note, many studies that have examined reward learning used a mixed valence feedback scheme. For reward learning, past studies found reduced learning performance in depression (Forbes, Shaw, & Dahl, 2007; Herzallah et al., 2013; Morris, Bylsma, Yaroslavsky, Kovacs, & Rottenberg, 2015; Robinson, Cools, Carlisi, Sahakian, & Drevets, 2012). Reduced reward learning was related to reduced striatal activation following positive PE (Robinson, Cools, Carlisi, et al., 2012). Further, both reduced striatal activations and reduced reward learning were a predictor of future depressive symptoms during adolescence (Forbes et al., 2007; Morgan, Olino, McMakin, Ryan, & Forbes, 2013). In terms of positive PE effects on learning, a simulation meta-analysis found lower learning rates, while results from the conventional meta-analysis shows only showed lower learning rates at trend (Pike & Robinson, 2022). Estimation tasks found a reduced positivity bias (Garrett et al., 2014; Sharot, Korn, & Dolan, 2011), which may implicate a reduced positivity and confirmation bias in depression during RL as well. Here, learning rates from positive and confirmatory PE are usually higher than from negative and disconfirmatory PE, which leads to higher learning performance compared to symmetric learning rates (Palminteri & Lebreton, 2022). In contrast, a developmental study found no effect of depressive symptoms on learning rate asymmetry (Nussenbaum, Velez, Washington, Hamling, & Hartley, 2022). In brief, both reward condition and positive PE effects suggest that reward learning may be reduced in depression, but the underlying computational mechanisms during RL are less clear. For punishment learning, two studies found higher learning performance relative to that of reward learning (Herzallah et al., 2013; Timmer, Sescousse, Van Der Schaaf, Esselink, & Cools, 2017). An acute lowering of serotonin levels in healthy individuals through depleting its precursor tryptophan lead to enhanced punishment learning, which suggests that low serotonin increase punishment learning, but do not affect reward learning (Cools, Robinson, & Sahakian, 2008; Robinson, Cools, & Sahakian, 2012). Indeed, depressed individuals with SSRI medication showed lower punishment learning relative to unmedicated depression individuals, but no differences in reward leaning (Herzallah et al., 2013). Studies with computational models examined either punishment learning rates or negative PE learning rates. Punishment learning rates showed inconsistent findings, with higher learning rates in depressed and anxious individuals compared to healthy controls (Aylward et al., 2019), or lower learning rates in depressed compared to healthy controls (Mukherjee, Filipowicz, et al., 2020). In terms of negative PE valence, a meta-analysis found higher learning rates in a simulation approach, while results in the conventional approach showed no effect (Pike & Robinson, 2022). Another study did not find changed learning rates in relation to depression, but a higher shift towards perceiving punishments as larger (Brown et al., 2021). To summarize, despite some inconsistencies in the literature, both punishment condition and negative PE effects point towards enhanced punishment learning in depression. Overall, the finding of both reduced reward learning and enhanced punishment learning in depression has led to the idea that depressed individuals differ in their subjective valuation and exhibit an exaggerated loss aversion compared to what prospect theory has established in healthy individuals (Chen, Takahashi, Nakagawa, Inoue, & Kusumi, 2015). There is evidence of learning deviations under both valence conditions in depression, but it is yet unclear whether these emerge in earlier developmental phases. DEP-RQ3: Are there differential effects of the anhedonic and negative affect symptoms clusters during RL? Depression is a highly heterogeneous disorder, which might explain some of the inconsistencies found during learning in depression. A closer look at specific symptom effects might prove useful to shed new light on the effects on RL. Depression is often distinguished by the symptom clusters of anhedonia and negative affect. Anhedonia is the loss of pleasure or lack of reactivity to pleasurable stimuli. While negative affect in depression shows considerable overlap with anxiety disorders, anhedonia is more independent of anxiety, but overlapping considerably with other disorders such as schizophrenia or addiction (Pizzagalli, 2014). Negative affect or distress putatively is influenced by serotonergic function, whereas anhedonia depends more upon dopaminergic function (Nutt, 2008). Therefore, these two symptom clusters within depression may have distinct effects on learning behavior. Since serotonergic function has been shown to specifically target punishment learning (Cools et al., 2008; Robinson, Cools, & Sahakian, 2012), it is possible that enhanced punishment learning can be linked to negative affect. A direct link between negative affect and punishment learning in depression has been previously reported, but remains understudied (Brown et al., 2021). Anhedonia in turn has been studied extensively, and consistently identified related reduced striatal activations during reward anticipation and reward delivery in adolescents and adults (Gradin et al., 2011; Stringaris et al., 2015). Anhedonic symptoms were related to less optimal switching behavior (Pizzagalli, Iosifescu, Hallett, Ratner, & Fava, 2008), reduced learning rates from positive and negative PE (Chase et al., 2010), specifically to reduced reward learning rates (Brown et al., 2021), and to more exploratory decision-making during learning (Harlé, Guo, Zhang, Paulus, & Yu, 2017). Overall, this suggests that anhedonic and negative affect symptoms clusters may have differential effects during RL, and further studies are needed to examine how negative affect modulates RL. Effects of Anxiety Anxiety disorders can differ with regard to the situation (e.g. social anxiety) or the objects (e.g. specific phobia) that cause symptoms such as aversive affective state, somatic stress symptoms and perception of sustained threat. Anxiety is increasingly recognized as developmental disorder (Leonardo & Hen, 2008), and together with depression and obsessive-compulsive disorder, it has been categorized as internalizing disorder, as opposed to externalizing disorders, e.g. ADHD and conduct disorder (Achenbach, 1966; Carver et al., 2017). Among internalizing disorders, conditions can be further divided into fear-based or distress-based symptom clusters (Watson, 2005), however, other symptom clusters have been distinguished as well (e.g. somatic and cognitive anxiety symptoms). Maladaptive uncertainty learning has been found for both the fear-based (Brown, Price, & Dombrovski, 2023) and the distress-based symptom clusters (Hammond, Xu, Ai, & Van Dam, 2023). A study that distinguished by somatic and cognitive symptom clusters found stronger influences of the somatic symptom cluster on RL (Fan, Gershman, & Phelps, 2022; Wise & Dolan, 2020). Neurally, individuals with anxiety disorders exhibited higher noradrenaline levels (Kalk, Nutt, & Lingford-Hughes, 2011) and dysregulated serotonergic modulation (Dayan & Huys, 2009), which may relate to the observed learning disruptions. Further, structural brain differences have been found to mediate the effect of unpredictability experienced during childhood on anxiety and depression symptoms in adults, which suggests that the uncertainty experienced as a child increases the risk for anxiety and depression and may lead to structural brain changes during development (Wang, Cao, Zheng, Chen, & Zhu, 2023). It remains unclear to what extent anxiety symptoms explain learning disruptions across both clinical and normative anxiety symptoms equally, and whether age modulates these effects during development. In this study, we will examine how effects of anxiety symptoms are present during RL under different levels of uncertainty and valence in both clinical and typically developing individuals. ANX-RQ1: What are the effects of anxiety on uncertainty conditions during RL? The current literature suggests that uncertainty processing is central to learning disruptions in anxiety symptoms (Brown et al., 2023). The aversive consequence of uncertainty in anxiety has been shown to impair behavioral learning performance already during low uncertainty, such as expected uncertainty, which is inherent in probabilistic but stable learning (LaFreniere & Newman, 2019). Under high uncertainty, such as in volatile environments, higher anxiety reduced learning performance and predicted aberrant switching behavior, such as reduced win-stay, increased or reduced lose-shift behavior (Dickstein et al., 2010; Hein, de Fockert, & Ruiz, 2021; Huang, Thompson, & Paulus, 2017; Piray, Ly, Roelofs, Cools, & Toni, 2019; Xia, Xu, Yang, Gu, & Zhang, 2021). Maladaptive uncertainty learning may be related to chronic underconfidence in individuals with anxiety and depression, as they showed disproportionally larger updates from low confidence decisions (Katyal, Huys, Dolan, & Fleming, 2023). One study also reported an inverted-u-shaped effect of trait anxiety on learning: both low and high trait anxiety were related to lower behavioral learning performance compared to intermediate trait anxiety (Aberg, Toren, & Paz, 2022). A nonlinear effect of anxiety on learning may be linked to the nonlinear effect of arousal on optimal task engagement that is attributed to noradrenaline function (Eckstein, Guerra-Carrillo, Miller Singley, & Bunge, 2017). Computational studies suggest that individuals with higher trait anxiety or with internalizing symptoms have difficulty adjusting their learning rate to the learning environments. Specifically, higher trait anxiety or internalizing symptoms predicted smaller learning rate adjustments between stable and volatile learning environments, which reflects less flexible learning (Browning, Behrens, Jocham, O’Reilly, & Bishop, 2015; Gagne, Zika, Dayan, & Bishop, 2020). The less flexible updating in relation to high internalizing symptoms was further characterized by a reduced updating after positive PE when action-outcome contingencies where changing throughout the learning block (Gagne et al., 2020). Beyond effects on learning rates, there is evidence that anxiety modulates decision-making during RL. Trait anxiety predicted the strategy by which participants explored alternative choices: higher trait anxiety participants exhibited a shift from value-based exploration towards uncertainty-related exploration (Aberg et al., 2022). This is in line with another study which reported that among individuals that were best described by a value-free choice strategy win-stay-lose-shift, higher state anxiety individuals exploited this value-free strategy more. Overall, anxiety may enhance less optimal choice strategies at the cost of value-based decision-making. To summarize, the currently literature found that anxiety has disruptive effects on learning under both low and high levels of uncertainty. Behavioral studies found reduced learning performance and less optimal switching behavior, but learning performance effects may also be nonlinearly related to anxiety symptoms. Computational studies have focused on anxiety effects between high and low uncertainty, but it is unclear how parameters of each uncertainty level are related to anxiety. These studies reported maladaptive learning rate adjustments to the statistics of the environment, as well as a shift from value-based to value-free decision behavior, but no studies have reported both effects of learning rate and decision behavior. Further, some studies reported combined effects of anxiety and depression, and it is not clear to what extent anxiety and depression show distinct effects on learning. Despite its relevance of anxiety disorders during development (Leonardo & Hen, 2008), it is unknown whether anxiety disrupts learning in children and adolescents similarly as in adults, since the only study with a pediatric sample found no behavioral effects of anxiety and did not apply computational models (Dickstein et al., 2010). This study will examine how anxiety symptoms in children and adolescents, both of clinical and normative symptoms range, show effects of uncertainty that are independent of depressive symptoms. ANX-RQ2: What are the effects of anxiety on valence conditions during RL? The effects of anxiety on uncertainty learning were most consistently found during punishment or threat learning, both behaviorally and computationally (Aberg et al., 2022; Browning et al., 2015; Gagne et al., 2020; LaFreniere & Newman, 2019; Piray et al., 2019). Some studies extended the found effects of uncertainty to both punishment and reward learning (Aberg et al., 2022; Gagne et al., 2020; LaFreniere & Newman, 2019) or did not find effects in either valence condition (Dickstein et al., 2010). Other studies did not examine reward and punishment separately, but they presented both rewards and punishments mixed within learning blocks; these studies found behaviorally reduced learning performance (Xia et al., 2021), and in a simulation meta-analysis higher negative PE learning rates and lower positive PE learning across depression and anxiety (Pike & Robinson, 2022). A recent study using naturalistic learning found that higher negative PE learning rates explained lower and less precise expectations about future academic outcomes and predicted the long-term development of anxiety (Villano et al., 2023). In brief, this new body of literature suggests that higher anxiety symptoms may predict aberrant processing of negative outcomes, both under punishment learning and from negative PE, and that this may be a risk factor for the development of anxiety. It is unknown whether anxiety modulates processing of negative outcomes already in children and adolescents. ANX-RQ3: Are there differential effects of anxiety symptom clusters during RL? The distinctions between fear-based and distress-based anxiety symptoms as well as somatic anxiety and cognitive anxiety symptoms have helped to examine learning disruptions in relation to more specific symptom clusters. Although it has been proposed that fear-based symptoms exert stronger effects on RL (Brown et al., 2023), the current literature suggests that multiple symptom clusters may have effects on RL. Studies that specified effects of symptom clusters found that somatic anxiety reduced uncertainty-directed exploration, whereas cognitive anxiety increased uncertainty-directed exploration (Fan et al., 2022). Similarly, divergent roles of these two symptoms clusters have been found during aversive learning (Wise & Dolan, 2020). The previously described maladaptive learning rate adjustment between stable and volatile environments was attributed to distress-based symptoms, which are less specific to anxiety and likely reflect internalizing symptoms more broadly (Gagne et al., 2020; Hammond et al., 2023). Importantly, the effects of fear-based symptoms have not been examined in these two studies. Therefore, it is unknown whether the effects on learning rate can be better attributed to fear-based symptoms rather than to distress-based symptoms. Since maladaptive learning rate adjustments were also linked to reduced pupil dilation changes, reflecting noradrenaline function and arousal, physiological anxiety symptoms may underlie uncertainty-related effects (Browning et al., 2015). The divergent effects of somatic and cognitive anxiety symptoms on decision behavior are difficult to interpret, but it seems that the role of somatic anxiety symptoms is more in line with the effect of a study that did not distinguish by symptom clusters (Aberg et al., 2022). The current definitions of symptoms clusters vary (eg. fear-based and distress-based symptoms, somatic and cognitive anxiety symptoms), which makes is difficult to integrate findings. Effects of ADHD Attention-deficit/hyperactivity disorder (ADHD) is commonly described as a developmental brain disorder, in which the hypofunctioning of noradrenaline and dopamine lead to reduced cognitive performance such as during RL (Plichta & Scheres, 2014; Ziegler, Pedersen, Mowinckel, & Biele, 2016). Notably, not just neurotransmitter hypofunction such as in ADHD, but also hyperfunction such as during stress (Arnsten, 1999, 2009) has been shown to impair performance. Therefore, an inverted-u-shaped relationship between neurotransmitter function and cognitive performance has been proposed (Biederman & Spencer, 1999; Del Campo, Chamberlain, Sahakian, & Robbins, 2011). Beyond functional differences, the effect of ADHD has been observed in the brain structure, likely as long-term consequence of brain neuroplasticity. Brain regions implicated in reward processing and value representation, such as the ventral striatum and the orbitofrontal cortex, exhibited smaller volumes in individuals with ADHD compared to controls (Carmona et al., 2009; Hesslinger et al., 2002). The well-established biological differences in ADHD have led to effective psychopharmacological treatments in ameliorating ADHD symptoms. Stimulants such as methylphenidate are used to target and normalize dopamine function. Some studies that controlled for methylphenidate use showed that it improved learning performance (Luman, Goos, & Oosterlaan, 2015; Pelham, Milich, & Walker, 1986), which suggests that medication use is a relevant confounding factor for examining effects of ADHD on learning. It remains unclear whether ADHD symptoms explain learning differences only by diagnostic categorization such as reported in case-control studies, or whether these learning effects also extend to subclinical ADHD symptoms in clinical and neurotypically developing groups. Further, it remains unclear whether the effects of ADHD on RL are more pronounced during sensitive developmental periods such as during childhood and adolescence. In this study, we will examine how effects of ADHD symptoms are present during RL under different levels of uncertainty and valence. ADHD-RQ1: What are the effects of ADHD on uncertainty conditions during RL? Several studies have examined the effects of ADHD diagnosis on RL under different levels of uncertainty (Hulsbosch et al., 2021). Under low uncertainty, studies showed mixed findings: some found no effects of ADHD (Luman et al., 2015; Oades & Müller, 1997; Wiesner, Molzow, Prehn-Kristensen, & Baving, 2017), while other found reduced learning (Frank, Santamaria, O’Reilly, & Willcutt, 2007; Gabay, Shahbari-Khateb, & Mendelsohn, 2018; Luman et al., 2021; Shephard, Jackson, & Groom, 2016). Additionally two studies reported slower and more variable reaction times (Frank et al., 2007; Gabay et al., 2018). Under high uncertainty, studies show similarly mixed evidence: two studies found no effects of ADHD (Chantiluke et al., 2015; Finger et al., 2008), one study reported computational effects (Hauser et al., 2014) in terms of a more explorative choice behavior, and two studies showed reduced learning performance in ADHD compared to a control group (Itami & Ca, 2002; Shephard et al., 2016). To summarize, the current literature shows mixed findings during RL under both low and high uncertainty. If an effect was found for ADHD, learning performance was reduced, choices were more erratic, and reaction times were slower and more variable. This study aims to shed new light on whether uncertainty modulates effects of ADHD symptoms during RL, and how these effects can be captured both behaviorally and computationally. ADHD-RQ2: What are the effects of ADHD on valence conditions during RL? No studies have examined the effects of punishment learning in ADHD so far. One study has reported differences in response to positive and negative feedback: negative feedback elicited stronger activations in children with ADHD compared to neurotypical children (Van Meel, Oosterlaan, Heslenfeld, & Sergeant, 2005). Given that the abovementioned brain deficits in ADHD were found in relation to reward learning, processing of negative feedback may be relatively spared and lead to an imbalance towards stronger processing of negative information compared to positive information. It is currently unknown whether the effects of ADHD might be less pronounced during punishment learning and learning from negative PE, compared to learning from reward and positive PE. ADHD-RQ3: Are there differential effects of the symptom clusters hyperactivity/impulsivity and inattentiveness during RL? ADHD consists of a set of symptoms, most notably the symptom clusters hyperactivity/impulsivity and inattentiveness. While previous studies reported correlations of the symptom clusters to learning deficits (Gabay et al., 2018; Luman et al., 2015), it is currently unclear whether the symptom clusters show separable contributions to RL. Computational models may help to identify separable contributions. For example, one theoretical model showed that aberrant learning parameters reproduced impulsive behavior in a delayed response time task (Williams & Dayan, 2005). Impulsive and hyperactive symptoms might be to learning parameters such as learning rate. No study has reported learning rate effects of ADHD, although all theoretical models of ADHD agree that dopamine hypofunction leads to aberrant learning parameters (Frank et al., 2007; Sagvolden, Johansen, Aase, & Russell, 2005; Tripp & Wickens, 2008). Thus, it is unknown whether hyperactivity/impulsivity symptoms and the underlying dopamine hypofunction specifically explain effects on learning rate. The attention deficit in ADHD has not been linked to computational parameters yet. However, noradrenaline function, which is fundamental in attentional processes, has been linked decision behavior during RL (Dubois et al., 2021, 2020; Frank et al., 2007). In contrast, one influential theoretical account of ADHD has linked attention deficits to the dopaminergic mesocortical pathway (Sagvolden et al., 2005). Therefore, it remains unclear whether inattentiveness in ADHD is specifically linked to noradrenergic function and attributed explorative decision behavior, as well as slower and more variable reaction times during RL.

Changes in the environment require constant adaptation to optimize future behavior. Learning through reinforcement is essential for adaptation in response to changes in the environment; such ability is thought to play a key role in human cognitive development as well as in the emergence and maintenance of psychiatric disorders, such as ADHD, depression or anxiety. Uncertainty and valence are considered to be two important factors for cognitive adaptation during reinforcement learning (RL). Clinical symptoms have been shown to modulate RL with respect to uncertainty and valence conditions, but little is known how psychopathology affects RL in clinically diagnosed compared to typically developing children and adolescents. In this study, we used a probabilistic learning task in which we experimentally manipulated uncertainty and valence levels. Uncertainty was manipulated by two uncertainty conditions, one which included stochasticity, and another which added volatility. Stochasticity or expected uncertainty refers to learning action-outcome contingencies that were probabilistic and stable (80% contingent and 20% non-contingent feedback). Uncertainty due to the probabilistic outcomes becomes expected once the preferred action-outcome contingency had been learned. Volatility further adds uncertainty to the probabilistic action-outcome contingencies: at change points, the learned action-outcome contingencies have to be reversed. Volatility represents unexpected uncertainty, since the exact points of the change cannot be anticipated. Valence has been examined by effects of both valence condition and prediction error (PE) valence which makes it challenging to compare and generalize results. In our study, our task allowed us to examine both these valence effects. For PE effects, valence is determined by whether the received outcome relative to the expected outcome was positive or negative at each trial (Eckstein, Master, Dahl, Wilbrecht, & Collins, 2022; Rosenbaum, Grassie, & Hartley, 2022). Notably, PE valence represents trial-to-trial valence effects, and learning from a positive PE may lead to a momentary positive surprise that can change quickly from positive to negative, if the next trial comes with a negative PE. For valence as block-wise condition effects, valence is determined by whether the absolute outcome value was positive or negative, such as in reward learning and punishment learning, respectively (Palminteri, Kilford, Coricelli, & Blakemore, 2016). Here, valence may unfold over multiple trials and lead to more enduring valence effects. During reward learning, the preferred outcome was to receive 3 coins compared to 1 coin only; during punishment learning, the loss of 1 coin was preferred than the loss of 3 coins. We also added a third condition of both mixed reward and punishments, which was commonly used in studies that only examined PE valence effects. In this third condition, which we will refer to as mixed condition, the preferred outcome was to receive 1 coin compared to losing 1 coin. Notably, PE valence can be additionally examined in any of the block-wise valence conditions. A pilot study indicated that the task can be applied to both clinical and neurotypical 8-18-year-old children and adolescents, and that their adaptation performance was modulated by valence and uncertainty. Our sample of the main study will include 120 children and adolescents between the age of 8 and 18, one clinical group (n=60) with diagnoses of anxiety, depression or ADHD, and a neurotypical group (n=60). We aim to delineate differences between psychiatric conditions (ADHD, anxiety, depression) for cognitive adaptation in respect to valence and uncertainty. We also aim to explore potential transdiagnostic effects of internalizing and externalizing psychopathology during cognitive adaptation. We will use both behavioral measures, such as accuracy, switching behavior and reaction time, as well as computational modeling measures, with the aim to capture adaptations in latent prediction-related parameters. How one learns in the learning conditions that differ by uncertainty and valence might depend upon the current symptom severity, both in clinical as well as neurotypical groups. Therefore, our aim of this study is to examine individual differences in clinical symptoms and their effects on uncertainty and valence during learning. We will further explore the moderating roles of the individual’s environment, state and trait, such as socioeconomic disparities, motivational traits and momentary affective state. Research Questions (RQ) Effects of Depression Major depressive disorder is an affective disorder whose primary symptoms are low mood and loss of motivation and pleasure in daily life. Despite its primarily affective symptoms, there is robust evidence of cognitive deficits in depression (Gotlib & Joormann, 2010), which may be related to the hypoactivity of serotonin, dopamine and noradrenaline (Eshel & Roiser, 2010; Ruhé, Mason, & Schene, 2007). Of note, depression is considered an internalizing disorder, as opposed to externalizing disorders (Achenbach, 1966; Carver, Johnson, & Timpano, 2017), with a large overlap in symptoms such as with anxiety disorders, specifically within a distress-based or negative affect symptom cluster (Watson, 2005). The overlap in negative affect symptoms between depression and anxiety may contribute to shared differences during RL. Although previous studies have found similarities and differences in the learning effects of depression and anxiety, a direct comparison with a task that manipulates both valence and uncertainty during RL has not been done. DEP-RQ1: What are the effects of depression on uncertainty conditions during RL? Studies that used RL with stable environments, reflecting low uncertainty, found reduced learning performance (Elliott, Sahakian, Herrod, Robbins, & Paykel, 1997; Steele, Meyer, & Ebmeier, 2004). Two studies used computational models to examine learning processes in depression, with in inconsistent results. The studies examined various parameters, including learning rate and reward sensitivity. Learning rate serves as a metric for quantifying how individuals update future values, whereas reward sensitivity reflects an individual's responsiveness to perceived outcomes. Reward learning rates were negatively related to anhedonic depression symptoms (Brown et al., 2021), but unrelated in a meta-analysis (Huys, Pizzagalli, Bogdan, & Dayan, 2013). Reward sensitivity in turn was either reduced (Huys et al., 2013) or increased (Brown et al., 2021). These contrasting findings may partly be explained by the use of a pavlovian and an instrumental learning task, respectively, and our study will focus on instrumental learning. One study reported reduced choice sensitivity in a more depressed student group (Kunisato et al., 2012), suggesting that depression is associated with less value-dependent choice behavior. Because the parameters of reward sensitivity and choice sensitivity are computationally interchangeable, current models cannot answer whether the parameters captured decision-related or feedback-related effects of depression (Browning, Paulus, & Huys, 2022). Additional parameter differences have been reported, such as a more negative valuation of received outcomes during punishment learning in depression (Brown et al., 2021), suggesting valence effects, or a decreased attentional breadth in relation to higher trait rumination (Hitchcock et al., 2022), suggesting impaired attention and memory processes. Further studies are needed to establish robust links between depressive symptoms and computational parameters in low uncertainty learning conditions during RL. Consistent with tasks of relatively low uncertainty, studies with more volatile tasks such as probabilistic reversal learning have reported impaired learning performance in depression (Dombrovski et al., 2010; Dombrovski, Szanto, Clark, Reynolds, & Siegle, 2013; Mukherjee, Filipowicz, Vo, Satterthwaite, & Kable, 2020; Mukherjee, Lee, Kazinka, D Satterthwaite, & Kable, 2020; Must, Horvath, Nemeth, & Janka, 2013; Rupprechter, Stankevicius, Huys, Steele, & Seriès, 2018). Less optimal switching behavior in term of lower win-stay and higher lose-switch behavior (particularly after misleading feedback) was also observed (Dickstein et al., 2010; Dombrovski et al., 2015; Mukherjee, Filipowicz, et al., 2020; Murphy, Michael, Robbins, & Sahakian, 2003; Taylor Tavares et al., 2008). In contrast, only one study also reported no learning differences in relation to depression (Brolsma et al., 2020). Some studies applied computational models, with inconsistent findings, similar to studies with low uncertainty tasks. One study reported reduced learning rates in depressed individuals compared to healthy controls (Mukherjee, Filipowicz, et al., 2020), while another study did not find such differences (Brolsma et al., 2020). Additionally, reduced choice sensitivity was reported (Mukherjee, Filipowicz, et al., 2020; Rupprechter et al., 2018), as well as a decreased memory of observed rewards, putatively due to working memory constraints (Rupprechter et al., 2018). Overall, behavioral findings show a relatively consistent picture of reduced learning performance in depression under both low and high uncertainty learning. However, these studies mostly used tasks of mixed-valence or reward conditions only, and the picture regarding uncertainty under punishment learning is less clear. Further, robust links between computational parameters, depression and uncertainty are yet to be established. There is some evidence that learning rate and choice sensitivity estimates are lower across uncertainty conditions. DEP-RQ2: What are the effects of depression on valence conditions during RL? Past RL studies of depression have usually used either conditions for reward and punishment learning separately, or they used PE valence within a reward task to determine valence effects (i.e. gaining more or less than expected). While both PE valence as well as valence condition (learning from positive or from negative feedback) have been studied extensively in depression, these valence effects may represent distinct characteristics on learning which need to be disentangled. To shed light on the relevance of the way valence is manipulated in relation to depression, our study includes 3 valence conditions: reward learning, punishment learning and mixed valence learning. Of note, many studies that have examined reward learning used a mixed valence feedback scheme. For reward learning, past studies found reduced learning performance in depression (Forbes, Shaw, & Dahl, 2007; Herzallah et al., 2013; Morris, Bylsma, Yaroslavsky, Kovacs, & Rottenberg, 2015; Robinson, Cools, Carlisi, Sahakian, & Drevets, 2012). Reduced reward learning was related to reduced striatal activation following positive PE (Robinson, Cools, Carlisi, et al., 2012). Further, both reduced striatal activations and reduced reward learning were a predictor of future depressive symptoms during adolescence (Forbes et al., 2007; Morgan, Olino, McMakin, Ryan, & Forbes, 2013). In terms of positive PE effects on learning, a simulation meta-analysis found lower learning rates, while results from the conventional meta-analysis shows only showed lower learning rates at trend (Pike & Robinson, 2022). Estimation tasks found a reduced positivity bias (Garrett et al., 2014; Sharot, Korn, & Dolan, 2011), which may implicate a reduced positivity and confirmation bias in depression during RL as well. Here, learning rates from positive and confirmatory PE are usually higher than from negative and disconfirmatory PE, which leads to higher learning performance compared to symmetric learning rates (Palminteri & Lebreton, 2022). In contrast, a developmental study found no effect of depressive symptoms on learning rate asymmetry (Nussenbaum, Velez, Washington, Hamling, & Hartley, 2022). In brief, both reward condition and positive PE effects suggest that reward learning may be reduced in depression, but the underlying computational mechanisms during RL are less clear. For punishment learning, two studies found higher learning performance relative to that of reward learning (Herzallah et al., 2013; Timmer, Sescousse, Van Der Schaaf, Esselink, & Cools, 2017). An acute lowering of serotonin levels in healthy individuals through depleting its precursor tryptophan lead to enhanced punishment learning, which suggests that low serotonin increase punishment learning, but do not affect reward learning (Cools, Robinson, & Sahakian, 2008; Robinson, Cools, & Sahakian, 2012). Indeed, depressed individuals with SSRI medication showed lower punishment learning relative to unmedicated depression individuals, but no differences in reward leaning (Herzallah et al., 2013). Studies with computational models examined either punishment learning rates or negative PE learning rates. Punishment learning rates showed inconsistent findings, with higher learning rates in depressed and anxious individuals compared to healthy controls (Aylward et al., 2019), or lower learning rates in depressed compared to healthy controls (Mukherjee, Filipowicz, et al., 2020). In terms of negative PE valence, a meta-analysis found higher learning rates in a simulation approach, while results in the conventional approach showed no effect (Pike & Robinson, 2022). Another study did not find changed learning rates in relation to depression, but a higher shift towards perceiving punishments as larger (Brown et al., 2021). To summarize, despite some inconsistencies in the literature, both punishment condition and negative PE effects point towards enhanced punishment learning in depression. Overall, the finding of both reduced reward learning and enhanced punishment learning in depression has led to the idea that depressed individuals differ in their subjective valuation and exhibit an exaggerated loss aversion compared to what prospect theory has established in healthy individuals (Chen, Takahashi, Nakagawa, Inoue, & Kusumi, 2015). There is evidence of learning deviations under both valence conditions in depression, but it is yet unclear whether these emerge in earlier developmental phases. DEP-RQ3: Are there differential effects of the anhedonic and negative affect symptoms clusters during RL? Depression is a highly heterogeneous disorder, which might explain some of the inconsistencies found during learning in depression. A closer look at specific symptom effects might prove useful to shed new light on the effects on RL. Depression is often distinguished by the symptom clusters of anhedonia and negative affect. Anhedonia is the loss of pleasure or lack of reactivity to pleasurable stimuli. While negative affect in depression shows considerable overlap with anxiety disorders, anhedonia is more independent of anxiety, but overlapping considerably with other disorders such as schizophrenia or addiction (Pizzagalli, 2014). Negative affect or distress putatively is influenced by serotonergic function, whereas anhedonia depends more upon dopaminergic function (Nutt, 2008). Therefore, these two symptom clusters within depression may have distinct effects on learning behavior. Since serotonergic function has been shown to specifically target punishment learning (Cools et al., 2008; Robinson, Cools, & Sahakian, 2012), it is possible that enhanced punishment learning can be linked to negative affect. A direct link between negative affect and punishment learning in depression has been previously reported, but remains understudied (Brown et al., 2021). Anhedonia in turn has been studied extensively, and consistently identified related reduced striatal activations during reward anticipation and reward delivery in adolescents and adults (Gradin et al., 2011; Stringaris et al., 2015). Anhedonic symptoms were related to less optimal switching behavior (Pizzagalli, Iosifescu, Hallett, Ratner, & Fava, 2008), reduced learning rates from positive and negative PE (Chase et al., 2010), specifically to reduced reward learning rates (Brown et al., 2021), and to more exploratory decision-making during learning (Harlé, Guo, Zhang, Paulus, & Yu, 2017). Overall, this suggests that anhedonic and negative affect symptoms clusters may have differential effects during RL, and further studies are needed to examine how negative affect modulates RL. Effects of Anxiety Anxiety disorders can differ with regard to the situation (e.g. social anxiety) or the objects (e.g. specific phobia) that cause symptoms such as aversive affective state, somatic stress symptoms and perception of sustained threat. Anxiety is increasingly recognized as developmental disorder (Leonardo & Hen, 2008), and together with depression and obsessive-compulsive disorder, it has been categorized as internalizing disorder, as opposed to externalizing disorders, e.g. ADHD and conduct disorder (Achenbach, 1966; Carver et al., 2017). Among internalizing disorders, conditions can be further divided into fear-based or distress-based symptom clusters (Watson, 2005), however, other symptom clusters have been distinguished as well (e.g. somatic and cognitive anxiety symptoms). Maladaptive uncertainty learning has been found for both the fear-based (Brown, Price, & Dombrovski, 2023) and the distress-based symptom clusters (Hammond, Xu, Ai, & Van Dam, 2023). A study that distinguished by somatic and cognitive symptom clusters found stronger influences of the somatic symptom cluster on RL (Fan, Gershman, & Phelps, 2022; Wise & Dolan, 2020). Neurally, individuals with anxiety disorders exhibited higher noradrenaline levels (Kalk, Nutt, & Lingford-Hughes, 2011) and dysregulated serotonergic modulation (Dayan & Huys, 2009), which may relate to the observed learning disruptions. Further, structural brain differences have been found to mediate the effect of unpredictability experienced during childhood on anxiety and depression symptoms in adults, which suggests that the uncertainty experienced as a child increases the risk for anxiety and depression and may lead to structural brain changes during development (Wang, Cao, Zheng, Chen, & Zhu, 2023). It remains unclear to what extent anxiety symptoms explain learning disruptions across both clinical and normative anxiety symptoms equally, and whether age modulates these effects during development. In this study, we will examine how effects of anxiety symptoms are present during RL under different levels of uncertainty and valence in both clinical and typically developing individuals. ANX-RQ1: What are the effects of anxiety on uncertainty conditions during RL? The current literature suggests that uncertainty processing is central to learning disruptions in anxiety symptoms (Brown et al., 2023). The aversive consequence of uncertainty in anxiety has been shown to impair behavioral learning performance already during low uncertainty, such as expected uncertainty, which is inherent in probabilistic but stable learning (LaFreniere & Newman, 2019). Under high uncertainty, such as in volatile environments, higher anxiety reduced learning performance and predicted aberrant switching behavior, such as reduced win-stay, increased or reduced lose-shift behavior (Dickstein et al., 2010; Hein, de Fockert, & Ruiz, 2021; Huang, Thompson, & Paulus, 2017; Piray, Ly, Roelofs, Cools, & Toni, 2019; Xia, Xu, Yang, Gu, & Zhang, 2021). Maladaptive uncertainty learning may be related to chronic underconfidence in individuals with anxiety and depression, as they showed disproportionally larger updates from low confidence decisions (Katyal, Huys, Dolan, & Fleming, 2023). One study also reported an inverted-u-shaped effect of trait anxiety on learning: both low and high trait anxiety were related to lower behavioral learning performance compared to intermediate trait anxiety (Aberg, Toren, & Paz, 2022). A nonlinear effect of anxiety on learning may be linked to the nonlinear effect of arousal on optimal task engagement that is attributed to noradrenaline function (Eckstein, Guerra-Carrillo, Miller Singley, & Bunge, 2017). Computational studies suggest that individuals with higher trait anxiety or with internalizing symptoms have difficulty adjusting their learning rate to the learning environments. Specifically, higher trait anxiety or internalizing symptoms predicted smaller learning rate adjustments between stable and volatile learning environments, which reflects less flexible learning (Browning, Behrens, Jocham, O’Reilly, & Bishop, 2015; Gagne, Zika, Dayan, & Bishop, 2020). The less flexible updating in relation to high internalizing symptoms was further characterized by a reduced updating after positive PE when action-outcome contingencies where changing throughout the learning block (Gagne et al., 2020). Beyond effects on learning rates, there is evidence that anxiety modulates decision-making during RL. Trait anxiety predicted the strategy by which participants explored alternative choices: higher trait anxiety participants exhibited a shift from value-based exploration towards uncertainty-related exploration (Aberg et al., 2022). This is in line with another study which reported that among individuals that were best described by a value-free choice strategy win-stay-lose-shift, higher state anxiety individuals exploited this value-free strategy more. Overall, anxiety may enhance less optimal choice strategies at the cost of value-based decision-making. To summarize, the currently literature found that anxiety has disruptive effects on learning under both low and high levels of uncertainty. Behavioral studies found reduced learning performance and less optimal switching behavior, but learning performance effects may also be nonlinearly related to anxiety symptoms. Computational studies have focused on anxiety effects between high and low uncertainty, but it is unclear how parameters of each uncertainty level are related to anxiety. These studies reported maladaptive learning rate adjustments to the statistics of the environment, as well as a shift from value-based to value-free decision behavior, but no studies have reported both effects of learning rate and decision behavior. Further, some studies reported combined effects of anxiety and depression, and it is not clear to what extent anxiety and depression show distinct effects on learning. Despite its relevance of anxiety disorders during development (Leonardo & Hen, 2008), it is unknown whether anxiety disrupts learning in children and adolescents similarly as in adults, since the only study with a pediatric sample found no behavioral effects of anxiety and did not apply computational models (Dickstein et al., 2010). This study will examine how anxiety symptoms in children and adolescents, both of clinical and normative symptoms range, show effects of uncertainty that are independent of depressive symptoms. ANX-RQ2: What are the effects of anxiety on valence conditions during RL? The effects of anxiety on uncertainty learning were most consistently found during punishment or threat learning, both behaviorally and computationally (Aberg et al., 2022; Browning et al., 2015; Gagne et al., 2020; LaFreniere & Newman, 2019; Piray et al., 2019). Some studies extended the found effects of uncertainty to both punishment and reward learning (Aberg et al., 2022; Gagne et al., 2020; LaFreniere & Newman, 2019) or did not find effects in either valence condition (Dickstein et al., 2010). Other studies did not examine reward and punishment separately, but they presented both rewards and punishments mixed within learning blocks; these studies found behaviorally reduced learning performance (Xia et al., 2021), and in a simulation meta-analysis higher negative PE learning rates and lower positive PE learning across depression and anxiety (Pike & Robinson, 2022). A recent study using naturalistic learning found that higher negative PE learning rates explained lower and less precise expectations about future academic outcomes and predicted the long-term development of anxiety (Villano et al., 2023). In brief, this new body of literature suggests that higher anxiety symptoms may predict aberrant processing of negative outcomes, both under punishment learning and from negative PE, and that this may be a risk factor for the development of anxiety. It is unknown whether anxiety modulates processing of negative outcomes already in children and adolescents. ANX-RQ3: Are there differential effects of anxiety symptom clusters during RL? The distinctions between fear-based and distress-based anxiety symptoms as well as somatic anxiety and cognitive anxiety symptoms have helped to examine learning disruptions in relation to more specific symptom clusters. Although it has been proposed that fear-based symptoms exert stronger effects on RL (Brown et al., 2023), the current literature suggests that multiple symptom clusters may have effects on RL. Studies that specified effects of symptom clusters found that somatic anxiety reduced uncertainty-directed exploration, whereas cognitive anxiety increased uncertainty-directed exploration (Fan et al., 2022). Similarly, divergent roles of these two symptoms clusters have been found during aversive learning (Wise & Dolan, 2020). The previously described maladaptive learning rate adjustment between stable and volatile environments was attributed to distress-based symptoms, which are less specific to anxiety and likely reflect internalizing symptoms more broadly (Gagne et al., 2020; Hammond et al., 2023). Importantly, the effects of fear-based symptoms have not been examined in these two studies. Therefore, it is unknown whether the effects on learning rate can be better attributed to fear-based symptoms rather than to distress-based symptoms. Since maladaptive learning rate adjustments were also linked to reduced pupil dilation changes, reflecting noradrenaline function and arousal, physiological anxiety symptoms may underlie uncertainty-related effects (Browning et al., 2015). The divergent effects of somatic and cognitive anxiety symptoms on decision behavior are difficult to interpret, but it seems that the role of somatic anxiety symptoms is more in line with the effect of a study that did not distinguish by symptom clusters (Aberg et al., 2022). The current definitions of symptoms clusters vary (eg. fear-based and distress-based symptoms, somatic and cognitive anxiety symptoms), which makes is difficult to integrate findings. Effects of ADHD Attention-deficit/hyperactivity disorder (ADHD) is commonly described as a developmental brain disorder, in which the hypofunctioning of noradrenaline and dopamine lead to reduced cognitive performance such as during RL (Plichta & Scheres, 2014; Ziegler, Pedersen, Mowinckel, & Biele, 2016). Notably, not just neurotransmitter hypofunction such as in ADHD, but also hyperfunction such as during stress (Arnsten, 1999, 2009) has been shown to impair performance. Therefore, an inverted-u-shaped relationship between neurotransmitter function and cognitive performance has been proposed (Biederman & Spencer, 1999; Del Campo, Chamberlain, Sahakian, & Robbins, 2011). Beyond functional differences, the effect of ADHD has been observed in the brain structure, likely as long-term consequence of brain neuroplasticity. Brain regions implicated in reward processing and value representation, such as the ventral striatum and the orbitofrontal cortex, exhibited smaller volumes in individuals with ADHD compared to controls (Carmona et al., 2009; Hesslinger et al., 2002). The well-established biological differences in ADHD have led to effective psychopharmacological treatments in ameliorating ADHD symptoms. Stimulants such as methylphenidate are used to target and normalize dopamine function. Some studies that controlled for methylphenidate use showed that it improved learning performance (Luman, Goos, & Oosterlaan, 2015; Pelham, Milich, & Walker, 1986), which suggests that medication use is a relevant confounding factor for examining effects of ADHD on learning. It remains unclear whether ADHD symptoms explain learning differences only by diagnostic categorization such as reported in case-control studies, or whether these learning effects also extend to subclinical ADHD symptoms in clinical and neurotypically developing groups. Further, it remains unclear whether the effects of ADHD on RL are more pronounced during sensitive developmental periods such as during childhood and adolescence. In this study, we will examine how effects of ADHD symptoms are present during RL under different levels of uncertainty and valence. ADHD-RQ1: What are the effects of ADHD on uncertainty conditions during RL? Several studies have examined the effects of ADHD diagnosis on RL under different levels of uncertainty (Hulsbosch et al., 2021). Under low uncertainty, studies showed mixed findings: some found no effects of ADHD (Luman et al., 2015; Oades & Müller, 1997; Wiesner, Molzow, Prehn-Kristensen, & Baving, 2017), while other found reduced learning (Frank, Santamaria, O’Reilly, & Willcutt, 2007; Gabay, Shahbari-Khateb, & Mendelsohn, 2018; Luman et al., 2021; Shephard, Jackson, & Groom, 2016). Additionally two studies reported slower and more variable reaction times (Frank et al., 2007; Gabay et al., 2018). Under high uncertainty, studies show similarly mixed evidence: two studies found no effects of ADHD (Chantiluke et al., 2015; Finger et al., 2008), one study reported computational effects (Hauser et al., 2014) in terms of a more explorative choice behavior, and two studies showed reduced learning performance in ADHD compared to a control group (Itami & Ca, 2002; Shephard et al., 2016). To summarize, the current literature shows mixed findings during RL under both low and high uncertainty. If an effect was found for ADHD, learning performance was reduced, choices were more erratic, and reaction times were slower and more variable. This study aims to shed new light on whether uncertainty modulates effects of ADHD symptoms during RL, and how these effects can be captured both behaviorally and computationally. ADHD-RQ2: What are the effects of ADHD on valence conditions during RL? No studies have examined the effects of punishment learning in ADHD so far. One study has reported differences in response to positive and negative feedback: negative feedback elicited stronger activations in children with ADHD compared to neurotypical children (Van Meel, Oosterlaan, Heslenfeld, & Sergeant, 2005). Given that the abovementioned brain deficits in ADHD were found in relation to reward learning, processing of negative feedback may be relatively spared and lead to an imbalance towards stronger processing of negative information compared to positive information. It is currently unknown whether the effects of ADHD might be less pronounced during punishment learning and learning from negative PE, compared to learning from reward and positive PE. ADHD-RQ3: Are there differential effects of the symptom clusters hyperactivity/impulsivity and inattentiveness during RL? ADHD consists of a set of symptoms, most notably the symptom clusters hyperactivity/impulsivity and inattentiveness. While previous studies reported correlations of the symptom clusters to learning deficits (Gabay et al., 2018; Luman et al., 2015), it is currently unclear whether the symptom clusters show separable contributions to RL. Computational models may help to identify separable contributions. For example, one theoretical model showed that aberrant learning parameters reproduced impulsive behavior in a delayed response time task (Williams & Dayan, 2005). Impulsive and hyperactive symptoms might be to learning parameters such as learning rate. No study has reported learning rate effects of ADHD, although all theoretical models of ADHD agree that dopamine hypofunction leads to aberrant learning parameters (Frank et al., 2007; Sagvolden, Johansen, Aase, & Russell, 2005; Tripp & Wickens, 2008). Thus, it is unknown whether hyperactivity/impulsivity symptoms and the underlying dopamine hypofunction specifically explain effects on learning rate. The attention deficit in ADHD has not been linked to computational parameters yet. However, noradrenaline function, which is fundamental in attentional processes, has been linked decision behavior during RL (Dubois et al., 2021, 2020; Frank et al., 2007). In contrast, one influential theoretical account of ADHD has linked attention deficits to the dopaminergic mesocortical pathway (Sagvolden et al., 2005). Therefore, it remains unclear whether inattentiveness in ADHD is specifically linked to noradrenergic function and attributed explorative decision behavior, as well as slower and more variable reaction times during RL.

The present research aims at contributing to the understanding of the different consequences of multi-attributional diversity and activation of stereotypes for targets and perceivers in CSCL groups in higher distance education. This is done by analysing self-reported and behavioral data.

The present research aims at contributing to the understanding of the different consequences of multi-attributional diversity and activation of stereotypes for targets and perceivers in CSCL groups in higher distance education. This is done by analysing self-reported and behavioral data.

The present research aims at contributing to the understanding of the different consequences of multi-attributional diversity and activation of stereotypes for targets and perceivers in CSCL groups in higher distance education. This is done by analysing self-reported and behavioral data.

The present research aims at contributing to the understanding of the different consequences of multi-attributional diversity and activation of stereotypes for targets and perceivers in CSCL groups in higher distance education. This is done by analysing self-reported and behavioral data.

A survey of informal learning among 318 teachers and HRD professionals was conducted. Analysis of the data found that teachers rely to a greater extent on interactive informal learning activities while HRD professionals rely to a greater extent on independent learning activities. Data analysis also found that six environmental factors inhibit engagement in informal learning and seven personal characteristics enhance motivation to participate in informal learning. Implications for HRD theory, research, and practice are discussed. (Contains 1 table.)

A survey of informal learning among 318 teachers and HRD professionals was conducted. Analysis of the data found that teachers rely to a greater extent on interactive informal learning activities while HRD professionals rely to a greater extent on independent learning activities. Data analysis also found that six environmental factors inhibit engagement in informal learning and seven personal characteristics enhance motivation to participate in informal learning. Implications for HRD theory, research, and practice are discussed. (Contains 1 table.)

A survey of informal learning among 318 teachers and HRD professionals was conducted. Analysis of the data found that teachers rely to a greater extent on interactive informal learning activities while HRD professionals rely to a greater extent on independent learning activities. Data analysis also found that six environmental factors inhibit engagement in informal learning and seven personal characteristics enhance motivation to participate in informal learning. Implications for HRD theory, research, and practice are discussed. (Contains 1 table.)

This study investigated the impact of a sequence of social relationship activities on regard for classmates and teammates in middle school (grade 7) mathematics classes using cooperative learning. The sample consisted of 184 students (55% Hispanic American, 27% White, 14% Black, and 3% Asian American) in a city in Los Angeles County (California). Two teachers each taught three classes; each teacher taught two experimental treatment (cooperative learning) classes and one conventional (comparison) class. Activities were sequenced and related to the following stages of group development: (1) class-building; (2) preparation for group work/team-building; (3) communication; and (4) cooperation and helping behaviors. Students in experimental groups also received instruction in effective explaining and problem solving. Overall, the sequence of interventions was effective in increasing students' regard for one another. Class-building increased students' regard for classmates, and team-building and activities to prepare for group work were effective in increasing students' regard for teammates and cross-ethnic and cross-gender regard. The differences between classes demonstrate how cooperative learning can differ in practice even when teachers have the same instructions and students have the same activities. Statistical data are presented in 12 tables. A 33-item list of references is included. (SLD)

This exemplary case study describes the learning process of two sixth-graders that learn from an animated worked-out example and an accompanying self-explanation prompt in the domain of fractions. It is based on a corresponding field study. The analysis focuses on the interaction with the computer, the communication between the students, the metacognitive aspects of the learning process and self-explanations. Supported with quantitative data, the qualitative results show that worked-out examples are proper materials for learning in groups of two. Furthermore, it is shown that self-explanation prompts have positive effects on the learning process and the analysed aspects. With detailed scenes it is elucidated, how the interactive capabilities and the animations are used during the learning process. [For the complete proceedings, see ED597799.]

A form of group learning, cohorts, has become increasingly attractive to administrators, instructors, and participants in adult education. Basic academic skills cohort learning supports three types of knowing: instrumental, socializing, and self-authoring; whereas, in higher and adult education cohort learning, the development of critical reflection and knowledge construction is the focus. Cohort learners report such positive effects as increased critical thinking skills, greater individual development, enhanced knowledge base, and learning motivation. Some research has revealed that not all cohorts work well, due to characteristics and behaviors of group members. Actual measurement of cohort learning outcomes has been inadequately studied. Educators can enhance the cohort experience by doing the following: (1) developing group relationships at the beginning; (2) balancing group and individual development; (3) providing an environment that both supports and challenges; and 4) acknowledging and addressing group and individual tensions. (Contains 12 references.) (AJ)

This booklet describes the learning experiences that take place in and as a result of a learning laboratory. Part one presents a brief history of the group dynamics movement, focusing especially upon the history of the National Training Laboratory of the National Education Association. Some significant characteristics of laboratory learning are presented, along with important ground rules of training group activity. Also, a brief review of some relevant learning theories is included. Part two describes the group dynamics laboratory at Indiana State University, its background and development, the staff, the students, and the training sessions. Part three is a report of the findings of a 1973 follow-up study of former laboratory participants that was designed to ascertain if the stated purposes of the course were being accomplished and whether the learning designs had any lasting effects. Finally, the present state of the laboratory approach to learning is summarized in part four. A bibliography is also included. (Author/RC)

The Peer Review (PR) is a very popular technique to support socio-constructivist and connectivist learning processes, online or face-to-face, at all educational levels, in both formal and informal contexts. The idea behind this technique is that sharing views and opinions with others by discussing with peers and receiving and providing formative feedback enriches the quality of learning. In this study, a class of trainee teachers conducts an online PR. The resulting interactions are analyzed and evaluated by the researchers through the application of an evaluation model based on both quantitative and qualitative data. In particular, two conditions are studied, namely the PR in groups versus the PR in dyads. Results show that students who carried out the PR in groups were less active from the cognitive point of view, while they devoted more effort to deal with organizational matters and discourse facilitation.

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The present study attempts to expand current question categorization schemes to identify question types that discriminate good learners from their peers in collaborative problem-solving groups. The study also explores the effects of person and task variables on students' question-asking behaviors in an effort to identify those that facilitate mathematics problem solving. Forty-seven fifth graders from two independent urban schools that use cooperative-learning methods participated in the study. Students were asked to solve fraction problems in one set containing continuous problems and in another set consisting of discrete problems. A question-categorization scheme was devised to code student requests to each other for information or assistance. No significant differences were found in the numbers of questions asked by students of high, low, or average ability. Although this appears contradictory to some previous results, it may be that including question types that were not exclusively help seeking explains the discrepancy. Overall, results indicate that type of task interacts with student characteristics and the setting to affect performance and students' peer-directed questions. Prior experience with cooperative-learning groups appears to have affected problem-solving and questioning performance. Two figures present study findings. (Contains 73 references.) (SLD)

The present study attempts to expand current question categorization schemes to identify question types that discriminate good learners from their peers in collaborative problem-solving groups. The study also explores the effects of person and task variables on students' question-asking behaviors in an effort to identify those that facilitate mathematics problem solving. Forty-seven fifth graders from two independent urban schools that use cooperative-learning methods participated in the study. Students were asked to solve fraction problems in one set containing continuous problems and in another set consisting of discrete problems. A question-categorization scheme was devised to code student requests to each other for information or assistance. No significant differences were found in the numbers of questions asked by students of high, low, or average ability. Although this appears contradictory to some previous results, it may be that including question types that were not exclusively help seeking explains the discrepancy. Overall, results indicate that type of task interacts with student characteristics and the setting to affect performance and students' peer-directed questions. Prior experience with cooperative-learning groups appears to have affected problem-solving and questioning performance. Two figures present study findings. (Contains 73 references.) (SLD)

The present study attempts to expand current question categorization schemes to identify question types that discriminate good learners from their peers in collaborative problem-solving groups. The study also explores the effects of person and task variables on students' question-asking behaviors in an effort to identify those that facilitate mathematics problem solving. Forty-seven fifth graders from two independent urban schools that use cooperative-learning methods participated in the study. Students were asked to solve fraction problems in one set containing continuous problems and in another set consisting of discrete problems. A question-categorization scheme was devised to code student requests to each other for information or assistance. No significant differences were found in the numbers of questions asked by students of high, low, or average ability. Although this appears contradictory to some previous results, it may be that including question types that were not exclusively help seeking explains the discrepancy. Overall, results indicate that type of task interacts with student characteristics and the setting to affect performance and students' peer-directed questions. Prior experience with cooperative-learning groups appears to have affected problem-solving and questioning performance. Two figures present study findings. (Contains 73 references.) (SLD)

This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 47 titles deal with a variety of topics, including the following: (1) the effect of source variation upon children's reactions to television commercials; (2) covert observation as a means of measuring the affective states of television viewers; (3) viewers' responses to a film as a socially situated event; (4) children's understanding of television reality; (5) media learning and adolescent developmental tasks; (6) the dynamics of communication and information in groups; (7) an information processing approach to uses and gratifications; (8) privacy and media encroachment; (9) the changing images of females and males in television commercials; (10) commercial broadcasters' perceptions of entry-level employment requirements for college graduates in the broadcast industry; (11) transnational advertising and the mass media in Latin America; (12) longitudinal patterns of television viewing and adolescent role socialization; (13) the relationship between persistence, attention, and observational learning of television's program content; (14) the film industry and the Vietnam war; (15) the effect of televised violence; (16) the use of videotape for studying nonverbal behavior in social settings; (17) United States Supreme Court interpretations of "public interest" in decisions concerning the media from 1927 to 1979. (FL)

This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 47 titles deal with a variety of topics, including the following: (1) the effect of source variation upon children's reactions to television commercials; (2) covert observation as a means of measuring the affective states of television viewers; (3) viewers' responses to a film as a socially situated event; (4) children's understanding of television reality; (5) media learning and adolescent developmental tasks; (6) the dynamics of communication and information in groups; (7) an information processing approach to uses and gratifications; (8) privacy and media encroachment; (9) the changing images of females and males in television commercials; (10) commercial broadcasters' perceptions of entry-level employment requirements for college graduates in the broadcast industry; (11) transnational advertising and the mass media in Latin America; (12) longitudinal patterns of television viewing and adolescent role socialization; (13) the relationship between persistence, attention, and observational learning of television's program content; (14) the film industry and the Vietnam war; (15) the effect of televised violence; (16) the use of videotape for studying nonverbal behavior in social settings; (17) United States Supreme Court interpretations of "public interest" in decisions concerning the media from 1927 to 1979. (FL)

preregistration

Group size and treatment intensity are understudied topics in mathematics intervention research. This study examined whether the treatment intensity and overall intervention effects of an empirically-validated Tier 2 mathematics intervention varied between intervention groups with 2:1 and 5:1 student-teacher ratios. Student practice opportunities and the quality of explicit instruction served as treatment intensity metrics. A total of 465 kindergarten students with mathematics difficulties from 136 intervention groups participated. Results suggested comparable performances between the 2:1 and 5:1 intervention groups on six outcome measures. Observation data indicated that the intensity of student practice opportunities differed by group size. Students in the 5:1 groups received more opportunities to practice with their peers, while students in the 2:1 groups participated in more frequent and higher quality individualized practice opportunities. Implications in terms of delivering Tier 2 interventions in small-group formats and engaging at-risk learners in meaningful practice opportunities are discussed. [This paper was published in "Journal of Learning Disabilities" v52 n2 p168-180 Mar 2019 (EJ1203634). The published article was titled "Examining the Impact of Group Size on the Treatment Intensity of a Tier 2 Mathematics Intervention within a Systematic Framework of Replication."]

We begin the article with a brief discussion of why groups are recommended in language teaching. After that, we describe what Cooperative Learning (CL) is. Then, we suggest ways in which CL and Information Technology (IT) fit well together. This is followed by some examples of how to combine CL with IT. [This article was published in Teaching of English Language and Literature," v13 n2 p5-8 1997.]

We begin the article with a brief discussion of why groups are recommended in language teaching. After that, we describe what Cooperative Learning (CL) is. Then, we suggest ways in which CL and Information Technology (IT) fit well together. This is followed by some examples of how to combine CL with IT. [This article was published in Teaching of English Language and Literature," v13 n2 p5-8 1997.]

We begin the article with a brief discussion of why groups are recommended in language teaching. After that, we describe what Cooperative Learning (CL) is. Then, we suggest ways in which CL and Information Technology (IT) fit well together. This is followed by some examples of how to combine CL with IT. [This article was published in Teaching of English Language and Literature," v13 n2 p5-8 1997.]

This practicum aimed to improve global awareness among middle school students. One specific goal was to increase the students' concept of the world and their global perspective. A second goal was to increase the students' geographic knowledge, enabling them to locate sites of current world events and affairs on a map or globe. Students were taught the geographic locational skills needed for learning the global issues and their localities in the world. The students synthesized facts about global interdependence. They wrote essays and reports derived from library research. Each student kept a current events notebook throughout the implementation of the practicum process. They were provided with 32 weeks of lessons, activities, and projects divided into 3 phases. Outcomes from this practicum experience were very positive. All six of the practicum's objectives were achieved and surpassed. The students gained knowledge of global interdependence. Their geographic skills improved. Finally, the students gained an interest in worldwide current events and affairs. The global awareness survey instrument and the geographic locational skills test are appended. (DB)

Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individuals, considering transaction histories and applying anomaly detection to identify suspicious behaviour. However, money laundering involves groups of collaborating individuals, and evidence of money laundering may only be apparent when the collective behaviour of these groups is considered. In this paper we describe a detection system that is capable of analysing group behaviour, using a combination of network analysis and supervised learning. This system is designed for real-world application and operates on networks consisting of millions of interacting parties. Evaluation of the system using real-world data indicates that suspicious activity is successfully detected. Importantly, the system exhibits a low rate of false positives, and is therefore suitable for use in a live intelligence environment.

The impact on achievement of (a) cooperative learning in which members discussed how well their group was functioning and how they could improve its effectiveness, (b) cooperative learning without any group processing, and (c) individualistic learning were compared on daily achievement, post-instructional achievement, and retention. Eighty-four third-grade students were randomly assigned to the three conditions stratifying for sex and ability level. The results indicate that the high-, medium-, and low-achieving students in the cooperation with group processing condition achieved higher on all three measures than did the students in the other two conditions. Students in the cooperation without group processing condition, furthermore, achieved higher on all three measures than did the students in the individualistic condition. Keywords include: Cooperation; Achievement; and Group Processing.

Compared were the characteristics of 60 learning disabled (LD) and 60 normal children (all between 8- and 11-years-old) participating in the Georgia Reading Research Program. The target group consisted of LD children who showed deficits in the psychological process of ordering/sequencing; while the LD reference group were average or above average in ordering/sequencing abilities. Instruments used to assess these deficits were the Wechsler Intelligence Scale for Children Sequencing Triad and the Wide Range Achievement Spelling Test. Other differences between the two groups were that the target group had instructional reading levels one or more years below their expected grade placement levels and were enrolled in special classes; while the LD reference children were enrolled in regular classes and were reading within six months of expected grade level. The program was to focus on an evaluation of specific curriculum treatments designed to facilitate reading achievement. (Author/DB)

This final report describes Project BLEND (Beginning Learning Experiences in Developmentally Inclusive Groups and at Home), a project designed to develop, implement, evaluate, and disseminate an ecological model for early intervention for children with developmental delays. Project BLEND included the following components: (a) the partnership (family, child care, BLEND) that was the context in which all activities for supporting each child's development were designed and implemented; (b) service coordination that assisted parents in establishing child care services and coordinating services from other agencies; and transition planning that assisted the child and family in making the transition to their next environment. In the first phase of the project, this model was developed and refined. In the second phase, the model was fully implemented with an existing early intervention center and community child care programs in middle Tennessee. In the third phase, the model was replicated in two sites: a school system in an urban setting that serves young children with disabilities (ages 3-5) and a second site that serves infants and toddlers with developmental delays and their families in a rural community. The report describes the project's activities and includes project implementation and replication checklists in the appendices. (Contains 12 references.) (Author/CR)

This study investigated congruence in ego stage and group relevant aspects of teachers' and learners' personalities, and between their preferences for and perceptions of group work and emotionality behaviors, as related to learners' learning achievement and to teacher and student satisfaction with learning group membership. Subjects (130 learners and seven teachers) were given the Ideal Group and Self-Description questionnaires, and measures of perception and satisfaction; each teacher also constructed a test of terminal student proficiency. Overall evidence from analysis of the ego-stage dyadic congruency, the group-relevant congruency, and the preference and perception congruency variables in association with teacher and learner satisfaction and learning achievement, did not form any assumptions as to their predictive relationships. The data did reveal some strength by the ego-stage dyadic congruency variable, especially in association with the dependent variables of learner achievement. Supplemental analysis of the eight ego stages indicated that teacher-learner dyadic congruency was not significantly related to learner satisfaction. Reasons for the findings were suggested, along with wider implications. Thesis is available from University Microfilms. (LY)

The aim of this paper is to present "the three steps method" of the self-formation of collaborative groups in a problem-based learning environment. The self-formation of collaborative groups is based on sharing of accountability among students for solving instructional problems. The steps of the method are planning collaborative problem solving, self-evaluation of students, and building collaborative groups. The planning comprises determination of the nomenclature of higher order thinking (HOT) skills, defining the instructional problems and their complexity levels, creating problem groups according to the complexity levels, setting the problem-relevant HOT skills, determining the accountability measure and the assessments of accountability for solving the problems. The self-evaluation includes self-detection of personal HOT skills, measurement of the diversity between the personal HOT skills and the problem-relevant skills based on the proposed diversity measure, and self-evaluation of willingness and desire of a student to take accountability for solving the instructional problems. The personal willingness is evaluated by the diversity measure. The desire is guided by the accountability assessments for problem solving. Coordination of the self-evaluation outcomes allows building collaborative groups. A group's composition is adjusted by the specific requirements of an instructor.

These revised Social Studies standards align with the College, Career, and Civic Readiness (C3) standards developed in partnership with the National Council for the Social Studies. In addition, the current version of the Learning Standards incorporates the Common Core State Standards and other revisions recommended by Washington's statewide cadre of K-12 social studies teachers. Created by Washington Social Studies teacher leaders, administrators, content experts, civic organizations, and stakeholder groups, these standards reflect the breadth and depth of Social Studies content. Perhaps the most important revision to the Social Studies standards is the movement of Social Studies skills to the forefront. While the former standards (revised in 2008) contained a skills section, the new standards recognize that the ability to understand and apply reasoning skills; apply research; deliberate, form, and evaluate positions are important skills not only for Social Studies, but for success in any discipline. Standards include: (1) Elementary School Standards by Discipline (Grades K-5); (2) Middle School Standards by Discipline (Grades 6-8); and (3) High School Standards by Discipline (Grades 9-12). [Cover title varies: "Social Studies Learning Standards."]

This publication reports on Communities in Crisis, a resource and adult education program designed to encourage local community leaders and volunteers to reflect critically upon their experiences and exchange ideas across different towns, cities, and regions in the United Kingdom. Part 1 describes the program and its three aims: sharing experiences, helping with project work, and building knowledge. Part 2 contains edited extracts from 15 project reports prepared by participants between 1983 and 1987. They are presented under three headings, each of which has represented a recurring focus for the preparation of project work in the programs. "Identifying needs and evaluating policies" refers to projects that involved participants in small-scale research investigations and data collection. "Case studies" features projects that focused on descriptions of organizational development and pressure group campaigns. "Reflection on personal experience" refers to projects in which participants use, value, and critically review their experiences of community work. Part 3 is a review of participants' gains; the practical use that has been made of the projects; and guidelines for the organization and planning of project work with community groups. Appendixes include a summary of research to evaluate motivation, study skills, and learning approaches of one group of Community in Crisis and a list of 21 references. (YLB)

This publication reports on Communities in Crisis, a resource and adult education program designed to encourage local community leaders and volunteers to reflect critically upon their experiences and exchange ideas across different towns, cities, and regions in the United Kingdom. Part 1 describes the program and its three aims: sharing experiences, helping with project work, and building knowledge. Part 2 contains edited extracts from 15 project reports prepared by participants between 1983 and 1987. They are presented under three headings, each of which has represented a recurring focus for the preparation of project work in the programs. "Identifying needs and evaluating policies" refers to projects that involved participants in small-scale research investigations and data collection. "Case studies" features projects that focused on descriptions of organizational development and pressure group campaigns. "Reflection on personal experience" refers to projects in which participants use, value, and critically review their experiences of community work. Part 3 is a review of participants' gains; the practical use that has been made of the projects; and guidelines for the organization and planning of project work with community groups. Appendixes include a summary of research to evaluate motivation, study skills, and learning approaches of one group of Community in Crisis and a list of 21 references. (YLB)

To develop adult learning groups in formal educational settings, the educator must understand the nature of learning in groups. Three types of group learning are instrumental, communicative, and emancipatory. The type of learning that occurs in groups varies according to the learning tasks and goals. Group learning that has as its goal the acquisition of instrumental knowledge is cooperative. The term collaborative describes group learning based on communicative knowledge. Transformative applies to learning groups that seek emancipatory knowledge. Cooperative learning focuses on the learning of individuals; as groups engage in collaborative or transformative learning, the distinction between individual and group learning becomes more invisible. The facilitator fosters, assists, supports, and helps with accomplishing learning tasks by sharing responsibilities with learners; establishes and maintains the group learning environment; and provides information about the group process. The facilitator's roles and responsibilities change to correspond to the group's purposes and goals. Size is an important characteristic of groups, with smaller groups (six or less) being more cohesive and productive. Facilitator-selected groups tend to perform better. Important considerations when structuring group learning for adults are the experience's purpose, an appropriate role for the facilitator, and group formation. (YLB)