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This study explores the characteristics and distribution of the feedback provided by the participants (a teacher and her students) in an activity organized inside a collaborative online learning environment. We analyse 853 submissions made by two groups of graduate students and their teacher (N1 = 629 & N2 = 224) involved in the collaborative development of a rubric for evaluating teaching skills using the Knowledge Forum platform. The results show that the feedback is distributed among participants (a teacher and her students), although there are important differences in the way in which this distribution occurs. The results also show that both the teacher and some of the students are able to provide verification and elaboration feedback on the learning content, the academic task at hand, and social participation. This feedback is useful for processes of knowledge construction, though significant differences are observed in the ways in which it is provided. Finally, the results show the importance of the temporal dimension for understanding how, when and for what purpose the teacher and students provide feedback to the other participants. (Contains 9 tables.)

Cohort groups in higher education have been established in response to the demographic shifts that have occurred with greater proportions of adult students and students who work off-campus. Cohort groups are defined as a group of students who begin coursework in a degree or certificate program together and who remain together for at least two-thirds of the classes in the program. This cohort learning study compared the learning outcomes of students in 12 matched groups, 6 representing degree programs scheduled in traditional, non-cohort formats and 6 representing the same degrees at the same institutions but in cohort formats. The institutions represented three large public research universities, one private research institution, and one private comprehensive institution. From a total of 353 students surveyed, usable response data came from 287. Learning outcomes were measured by grade point averages and the results of a student self-survey. Comparison between all cohort and non-cohort groups showed slightly higher cohort student learning on affective, cognitive, and learning transfer dimensions. Overall, there were more similarities than differences in learning outcomes between the cohort and non-cohort students surveyed. One figure and seven tables of data are appended. (Contains 25 references.) (JLS)

The Peer Assisted Learning (PAL) program at the University of Minnesota is a primary academic support program for historically difficult, introductory college courses that serve as gatekeepers to academic degree programs. Based upon operating principles of other academic support programs and educational theories, PAL is integrated into the courses it serves. The PAL groups review essential course content, model cognitive learning strategies to deepen understanding, and promote metacognitive awareness so students are autonomous learners in courses without academic support services. The PAL approach operates at the confluence of collaborative learning, cooperative learning groups, and learning communities. This article provides a detailed overview of the PAL model, educational theories upon which it is based, and how variations of it are implemented at the institution. Quantitative and qualitative studies reveal academic and personal benefits for participating students and those serving as PAL facilitators. The studies validate the role of PAL with closing the achievement gap between students of different ethnicities and levels of academic preparedness for rigorous college courses in mathematics and science.

The Peer Assisted Learning (PAL) program at the University of Minnesota is a primary academic support program for historically difficult, introductory college courses that serve as gatekeepers to academic degree programs. Based upon operating principles of other academic support programs and educational theories, PAL is integrated into the courses it serves. The PAL groups review essential course content, model cognitive learning strategies to deepen understanding, and promote metacognitive awareness so students are autonomous learners in courses without academic support services. The PAL approach operates at the confluence of collaborative learning, cooperative learning groups, and learning communities. This article provides a detailed overview of the PAL model, educational theories upon which it is based, and how variations of it are implemented at the institution. Quantitative and qualitative studies reveal academic and personal benefits for participating students and those serving as PAL facilitators. The studies validate the role of PAL with closing the achievement gap between students of different ethnicities and levels of academic preparedness for rigorous college courses in mathematics and science.

The Peer Assisted Learning (PAL) program at the University of Minnesota is a primary academic support program for historically difficult, introductory college courses that serve as gatekeepers to academic degree programs. Based upon operating principles of other academic support programs and educational theories, PAL is integrated into the courses it serves. The PAL groups review essential course content, model cognitive learning strategies to deepen understanding, and promote metacognitive awareness so students are autonomous learners in courses without academic support services. The PAL approach operates at the confluence of collaborative learning, cooperative learning groups, and learning communities. This article provides a detailed overview of the PAL model, educational theories upon which it is based, and how variations of it are implemented at the institution. Quantitative and qualitative studies reveal academic and personal benefits for participating students and those serving as PAL facilitators. The studies validate the role of PAL with closing the achievement gap between students of different ethnicities and levels of academic preparedness for rigorous college courses in mathematics and science.

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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.)

Collaborative learning in an online classroom can take the form of discussion among the whole class or within smaller groups. This paper addresses the latter, examining first whether assessment makes a difference to the level of learner participation and then considering other factors involved in creating effective collaborative learning groups. Data collected over a three year period (15 cohorts) from the Foundations course in the Master of Distance Education (MDE) program offered jointly by University of Maryland University College (UMUC) and the University of Oldenburg does not support the authors' original hypothesis that assessment makes a significant difference to learner participation levels in small group learning projects and leads them to question how much emphasis should be placed on grading work completed in study groups to the exclusion of other strategies. Drawing on observations of two MDE courses, including the Foundations course, their extensive online teaching experience, and a review of the literature, the authors identify factors other than grading that contribute positively to the effectiveness of small collaborative learning groups in the online environment. In particular, the paper focuses on specific instructional strategies that facilitate learner participation in small group projects, which result in an enhanced sense of community, increased skill acquisition, and better learning outcomes. (Contains 2 figures and 1 table.)

This report presents conceptual frameworks for inclusive learning, good practices in learning cities and recommendations for the future. The COVID-19 pandemic has had an enormous impact on life in cities, as well as exposing and exacerbating almost all forms of inequality. Access to high-calibre, resilient infrastructure, reliable basic services and decent jobs must be provided to all urban and rural dwellers. Achieving this means ensuring learning opportunities in cities are of high quality, inclusive of the diverse backgrounds of all learners and offered on a continuous basis throughout life. This publication features chapters on learning cities' endeavours to promote inclusive lifelong learning for vulnerable groups. It is based on research papers prepared for the fourth International Conference on Learning Cities, which took place in 2019 in Medellín, Colombia, under the theme 'Inclusion -- A principle for lifelong learning and sustainable cities' and hence marks a transition between the learning city conferences of 2019 and 2021. Though the examples included in the publication were in place before the pandemic took hold, they show how populations that were made even more vulnerable by the pandemic can be effectively targeted by lifelong learning opportunities.

Human language and cognition do not develop independently of each other but are intricately intertwined in various ways. This contribution presents the interplay between linguistic and cognitive abilities of learners at the individual level and relates them to the level of external contextual factors in social and educational environments. In empirical research, it is important to differentiate between so-called proximal and distal external factors: A causal effect on the individual learner can be primarily assumed through the direct stimulation of proximal factors, i.e., in personal interaction (Proximity of Stimulation approach, Kersten 2020, 2023). Therefore, external factors should be differentiated and special attention should be given to the proximal factors as direct influencing factors in empirical studies. This contribution begins with some terminological clarifications. In the following section, the connections between individual linguistic and cognitive development are discussed using selected variables in both directions (internal perspective). Initially, findings regarding the influence of multilingualism on cognitive abilities are examined, followed by the influence of cognitive abilities on L2 acquisition in the reverse direction. Finally, the contribution focuses on external contextual factors. Findings on family contextual factors such as linguistic and social background are presented. Lastly, educational contextual factors are explored, particularly discussing whether and how instructional factors can contribute to creating conducive conditions for mutual development in both domains, cognition and language, and potentially compensating for disadvantaged learner groups (cf. Kersten 2019). [This is the translated version of originally published title "Der Zusammenhang von kognitiven Fähigkeiten und Mehrsprachigkeit im frühen L2-Erwerb: Individuelle und externe Variablen," which was published in: "Language Education and Acquisition Research: Focusing Early Language Learning," edited by H. Böttger et al., Klinkhardt, 2020, pp. 82-116.]

This action research examines how focused organization, group roles, and gender grouping impact student participation when working in a cooperative group setting. Fifty-two sixth graders were studied for a period of nine weeks. Results show when students are organized in their cooperative groups, there will be an increase in student participation. Participation also increased when students were given assigned roles. Lastly, this research shows that my hypothesis was incorrect by thinking participation would increase when students work in same gender cooperative groups. To come to these results, data was collected using a triangular approach focusing on observations, change in grades, and questionnaires. The following are appended: (1) Data Collection Matrix; (2) Teacher Observation Log: Focused Organization; (3) Focused Organization; (4) Teacher Observation Log: Group Roles; (5) Student Roles; (6) Group Roles; (7) Teacher Observation Log: Gender Grouping; and (8) Gender Grouping. (Contains 5 figures.)

This multi-method study explored how children conceptualize emergent leadership in collaborative learning groups, and whether emergent leadership was associated with student achievement motivation. Fourth and fifth grade students participated in a collaborative math activity. After the group math task, 294 students were surveyed on their achievement orientation and emergence of leadership. Within their learning groups, a subset of 18 students was individually interviewed. The interview data revealed that elementary school- aged children are aware of the emergence of leadership in collaborative learning groups, describing leadership behaviors in two domains: task-focused and relationship-focused. The survey data revealed that while task-focused leadership was only associated with performance goal orientations. Relationships-focused leadership was associated with both mastery and performance goals, though the association was stronger with mastery goals. Taken together, this study shows the importance of including emergent leadership in the study of collaborative learning groups. (Contains 39 references.) (Author)

The Centre for Research and Learning in Regional Australia is investigating the elements of social capital and developing a set of indicators that show when social capital is building. The indicators can be used where groups or organizations with a shared purpose engage in productive interactions that benefit not only the individual member groups but also the "learning community" as a whole. The intention is that the indicators will be applicable to geographic communities, professional or common interest communities, such as professional associations, and groups of businesses such as Executive Link[TM], the subject of this paper. Executive Link consists of farm businesses that meet for nonformal training in several chapters in eastern Australia. Each chapter consists of about six farm businesses and their owner/managers who share their business management expertise. Executive Link appears to have features of a learning community, including a shared purpose and learning interactions across the boundaries of individual member businesses and chapters. This paper identifies networks, commitments, and shared values as the elements of social capital that contribute to the quality of learning interactions. Suggested indicators of social capital building include development of a shared language, shared experiences, trust, self-development, and an identification with the community. (Contains 25 references.) (Author/TD)

This study examined whether the intensive time-series design would yield data discriminating between concrete and formal operational students and whether achievement curves exhibited in previous studies would be found with students in this study. Daily measurement of variables is characteristic of the data collection procedure in the time-series design, where individual student data are collapsed into a group mean and treated during analysis as a single subject. Data were collected for 56 school days from 95 earth science students studying a unit on plate tetonics. To determine if the design would discriminate between formal and concrete operational students (identified by Lawson's Test of Formal Reasoning), each student responded daily to a single multiple-choice item (obtained from a pool of 78 items) measuring knowledge or understanding achievement. Two parallel forms (KR20=0.80) of a multiple-choice achievement test (consisting of 45 items) were also designed from the pool of 78 items. General knowledge and understanding achievement scores for each day were obtained and analyzed, indicating the precision of the design to discriminate between students differing in cognitive ability and supporting its use in monitoring the daily acquisition of knowledge related to a concept. Results also support the validity of the design for obtaining achievement data. (JN)

When gathering data for the design and evaluation of physical learning environments, there is a significant challenge in traversing, or translating, architect/designer language and stakeholders' verbal accounts of their expectations, preferences and experiences. In a series of studies used to provide data for both the evaluation of existing spaces and the design of future spaces, the authors utilized three focus group methods that incorporated elements of participatory design and visual play in activities. This paper describes these methods and the efficacy of each method in the context of physical learning environment evaluation and design.

When gathering data for the design and evaluation of physical learning environments, there is a significant challenge in traversing, or translating, architect/designer language and stakeholders' verbal accounts of their expectations, preferences and experiences. In a series of studies used to provide data for both the evaluation of existing spaces and the design of future spaces, the authors utilized three focus group methods that incorporated elements of participatory design and visual play in activities. This paper describes these methods and the efficacy of each method in the context of physical learning environment evaluation and design.

When gathering data for the design and evaluation of physical learning environments, there is a significant challenge in traversing, or translating, architect/designer language and stakeholders' verbal accounts of their expectations, preferences and experiences. In a series of studies used to provide data for both the evaluation of existing spaces and the design of future spaces, the authors utilized three focus group methods that incorporated elements of participatory design and visual play in activities. This paper describes these methods and the efficacy of each method in the context of physical learning environment evaluation and design.

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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 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

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.

There is growing evidence of the special benefits to be derived from self-directed learning groups, in which students operate without an instructor, determining for themselves the rate and manner in which to study course material and to evaluate their performance. At Hope College, 54 students enrolled in a social psychology course in Fall 1966 were randomly assigned to groups of 6 after undergoing pre-testing, completing a pre-course questionnaire, and receiving a detailed syllabus, explanation of course requirements and grading procedures and a manual on small group discussion. Groups met once a week and turned in individually completed sheets reporting feelings toward the group and the particular meeting. About 1/3 to 1/2 the meetings were held in an observation room where the students were observed (from behind 1-way mirrors), tape recorded and videotaped. A voluntary meeting of the entire group took place every 2 weeks. Once a week, the professor was available for free discussion. Students took a final exam on course content and evaluated their own as well as individual group member's progress and contribution. Final grades "ere determined by exam performance, a paper or project, group member evaluation, and self-evaluation. Results were generally positive. Students and investigator learned much about group dynamics, critical thinking was better and the students' satisfaction with the course was high. Students having low GPAs reported better study than in other similar courses. There were some negative reactions but evidence indicates that small group interaction combined with established values of traditional teaching techniques produces an educational experience that is total, and not merely academic. (JS)

This publication includes two papers by John Hollifield, three ERIC Digests and two ERIC resource lists on group learning, and a reprint of an ERIC search on cooperative learning in elementary and middle schools. The two papers are titled: (1) "Cooperative Learning in Elementary Schools: From Supplemental Instructional Practice to Schoolwide Restructuring"; and (2) "Trends in Elementary and Early Childhood Education." The first paper briefly describes a number of cooperative learning processes and examines the increasing pattern of use of cooperative learning processes in elementary schools over the last decade, as exemplified by the Johns Hopkins University Student Team Learning Processes. The processes are described, the research concerning them is summarized, and the existing knowledge about the use of the processes in elementary schools is discussed. Preceeded by a discussion of the school reform movement and demographic factors delineating the population of children, the trends identified and briefly discussed in the second paper are those currently influencing kindergarten, preschool education, child care, and elementary education. Digests and resource lists on ability grouping, cooperative learning strategies in children, mixed-age grouping and cooperative learning, and classroom and group interaction, and an ERIC search reprint containing 39 citations and abstracts, are included. (RH)

In this paper the design of a workflow support tool for competence based distance learning in a group setting is discussed. The design is based on a stakeholder analysis and crash-tested in an actual course setting. Preliminary findings suggest that some well-known problems have been solved, but further more in depth research is needed to assess the quality of the design with respect to more subtle issues.

This paper identifies differences between the three ethnic groups, namely, Kadazans/Dusuns, Bajaus, and other minority ethnic groups on the beliefs about learning English as a second language based on the five variables, that is, language aptitude, language learning difficulty, language learning and communicating strategies, nature of language learning as well as learning motivation and expectation. A modified version of Horwitz's (1987) Beliefs about Language Learning Inventory (BALLI) was distributed to all the 254 form four students in Lahat Datu but only 193 students responded. This survey instrument consists of 34 items. Results of a One-way ANOVA test showed a significant difference among ethnic groups on motivation and expectation in learning English. Bajau students showed the highest motivation and expectation in learning English, followed by other ethnic minorities compared to Kadazan/Dusun students. Although participants scored high for motivation and expectation to learn English, they perceived that English is a difficult language to acquire. BALLI is used to identify the misconceptions or beliefs held among learners and find ways to reduce the negative impacts in learning English.

This paper identifies differences between the three ethnic groups, namely, Kadazans/Dusuns, Bajaus, and other minority ethnic groups on the beliefs about learning English as a second language based on the five variables, that is, language aptitude, language learning difficulty, language learning and communicating strategies, nature of language learning as well as learning motivation and expectation. A modified version of Horwitz's (1987) Beliefs about Language Learning Inventory (BALLI) was distributed to all the 254 form four students in Lahat Datu but only 193 students responded. This survey instrument consists of 34 items. Results of a One-way ANOVA test showed a significant difference among ethnic groups on motivation and expectation in learning English. Bajau students showed the highest motivation and expectation in learning English, followed by other ethnic minorities compared to Kadazan/Dusun students. Although participants scored high for motivation and expectation to learn English, they perceived that English is a difficult language to acquire. BALLI is used to identify the misconceptions or beliefs held among learners and find ways to reduce the negative impacts in learning English.

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)

It can be difficult to find adequate strategies when teaching problem solving in a standard based mathematics classroom. The purpose of this study was to improve students' problem solving skills and attitudes through differentiated instruction when working on lengthy performance tasks in cooperative groups. This action research studied for 15 days whether students in a treatment group (n = 28), who were grouped by learning styles (auditory, kinesthetic, and visual), would display greater ability learning the standards or display better attitudes towards problem solving when compared to a control group (n = 28) who were grouped in random cooperative groups. When the qualitative and quantitative data were analyzed, the results demonstrated that the treatment group did not show significant gains when compared to random cooperative groups. The following are appended: (1) Instructional Unit Plan: Quadratic Investigations; (2) Peer-Review Instructional Plan Rubric; (3) Unit 5 Math I Test: Factoring and Solving Quadratics; (4) Student Problem Solving Attitudes Scale; (5) Performance Task Answer Sheet; (6) Product Grading Rubric; (7) Learning Channel Preference; (8) Learning Style Menu; (9) Note Taking for Reflective Journal; (10) Reflective Journal Prompts; and (11) Learning Style Survey. (Contains 12 tables.)

Computer simulations of catalytic networks. Andrade et al. (1993) have recently published the results of our first simulations, and have addressed the problem of the effect that catalytic error has in controlling system dynamics. Simulations of large networks are being designed in order examine spatio-temporal dynamics in reaction-diffusion systems. The aim is to develop visualization and analysis methods to apply large networks composed of biologically realistic neurons. Immunohistochemical studies have examined mammalian tissues that may be useful as model systems to examine distributed function in neurotransmission and neuromodulation (Soinila and Mpitsos, 1992; Soinila et al., 1992). It is necessary, as these and other publications (e.g., Mpitsos and Soinila, 1993) indicate, not only to understand neural organization in a simple animal, but also to examine the applicability of the findings to higher animals, and, if possible, to humans. Molecular biological studies of muscarinic receptors: In previous AFOSR-published work, Murray et al. (1985) and Murray and Mpitsos (1988) showed further that brief pharmacologic blocking of these receptors enhances 1-Trial associative learning. Over the past year, we have developed cloning vectors for generating fusing proteins to all of the five known muscarinic receptor subtypes in humans. Our next step is to obtaine immunofluorescent antisera to the fusion proteins in order to visually identify cells containing the different muscarinic receptors. The in-between step will be to determine the specificity of the antisera. The findings will be applicable not only to our experimental animal, but also to studies of learning and pathologies in humans.

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)

Despite a great deal of time and energy went into digitalisation of the world around us, education has been lagging behind. A question therefore arises to what extent higher education institutions should introduce e-learning as part of their programmes. The purpose of this study is to add to the body of knowledge on e-learning by examining perceptions and intentions of students regarding e-learning. There are two broader research objectives pursued in order to achieve the purpose. The first objective is to identify students' knowledge and perception of e-learning, along with their attitudes and experience with it. The second research objective is to assess readiness of students to engage in e-learning and determine their willingness to pay for it. This study uses mixed method research design. First, results of two focus groups are reported, followed by results of survey on 104 respondents in a country of Central Europe. Results show that, students are in general positively inclined towards e-learning and would be willing to take online courses. However, there are still some reservations connected to it and the preference is towards the blended format. In spite of e-learning's European roots, the majority of students do not want the full integration of Information Technology into the study process, meaning that the traditional learning methods combined with IT are preferred.

It has been documented that Facebook is the most popular social networking site among students. Given that most students are already users of Facebook, implementing it into the curriculum provides an easy way for students to actively participate in class activities. This paper explores the idea that the use of Facebook Groups to complement classroom teaching leads to a more meaningful learning experience and increases student engagement in finance. The instantaneous availability of course notes, problem solutions, PowerPoint slides, videos, and all course content to students on Facebook increases their interest in the subject matter and has a positive effect on learning. This paper presents an innovative method of using Facebook Groups to support teaching and learning in finance, and indeed in many other subject areas. The article reviews the literature on the use of Facebook in education and presents a model for effectively integrating Facebook Groups into the finance curriculum.

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)

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.

Collective intelligence, which aggregates the shared information from large crowds, is often negatively impacted by unreliable information sources with the low quality data. This becomes a barrier to the effective use of collective intelligence in a variety of applications. In order to address this issue, we propose a probabilistic model to jointly assess the reliability of sources and find the true data. We observe that different sources are often not independent of each other. Instead, sources are prone to be mutually influenced, which makes them dependent when sharing information with each other. High dependency between sources makes collective intelligence vulnerable to the overuse of redundant (and possibly incorrect) information from the dependent sources. Thus, we reveal the latent group structure among dependent sources, and aggregate the information at the group level rather than from individual sources directly. This can prevent the collective intelligence from being inappropriately dominated by dependent sources. We will also explicitly reveal the reliability of groups, and minimize the negative impacts of unreliable groups. Experimental results on real-world data sets show the effectiveness of the proposed approach with respect to existing algorithms.

This project seeks to find out whether giving students a choice in some aspect of their learning benefits them, to find out whether having a choice benefits certain groups of students, and to find out whether certain choices benefit certain groups of students.

Paired-associate learning efficiency was assessed within four low-SES ethnic populations (black, Chinese-American, Latino-American, and white) as a function of presentation conditions and method of measurement (verbal recall vs. pictorial recognition). A mixed-list paired-associate task was administered individually to 40 second grade children from each group. The results revealed substantial effects for presentation conditions, but not for populations. Nevertheless, the pattern of conditions effects differed as a function of both populations and measurement method. An explanation of the results was discussed in terms of the concept of differential memory coding as a function of specific subject characteristics. (References, tables, and figures are appended.) (Author)

The current study aims at investigating the impact of homogeneous versus heterogeneous collaborative learning grouping in multicultural classes on the students' achievements and attitudes towards learning science. In the present study, heterogeneity was unpacked through two dimensions: the cultural background, represented by the different nationalities present in the class and the students' different abilities. The interaction between these two factors and their combined effect on the achievement and attitudes were also investigated. The study also considered an approach to provide quality teaching for a diverse group of students by neutralizing the heterogeneity factor or reducing its negative effect. For this purpose, 100 nine graders from more than 10 countries in an independent preparatory school in Doha / Qatar were divided into four classes and distributed over the following learning "STAD" groups: (1) Heterogeneous by ability but homogeneous by nationality; (2) Heterogeneous by nationality but homogeneous by ability; (3) Entirely heterogeneous (i.e. by both the ability and the nationality); and (4) Entirely homogeneous (i.e. by both the ability and the nationality). A diagnostic placement test, standardized pretest and posttest in addition to the regular school tests were used to measure the achievement of the students. A Questionnaire was developed to measure the attitudes of the students towards learning science as well as towards group working. The study concluded that the main effects of group structure on the students' attitudes towards learning science were demonstrated by the heterogeneous group. It affected all the attitude components except the "working with students from different cultural backgrounds" dimension, where nearly all group types had the same effect. However, this positive attitude was enhanced when the effect of mixed ability classes was combined with the effect of multiculturalism. Having foreign students or students from different cultures in a mixed ability class, yielded the best desired results. Therefore, the researcher highly recommends maximizing the heterogeneity in a class in all possible ways. The implemented collaborative learning strategy made learning more fun and beneficial for the students, enhanced their self confidence, academic awareness, and consequently their overall attitude towards science. The heterogeneity factor had a negative effect on the achievement of the students. The students in the mixed ability classes scored less than the students in other groups. However, when the "same ability" groups contained students from different cultural backgrounds, the results were the most favorable. The optimum class composition that may yield best achievement results and constructs positive attitudes is a compromise that maximizes group diversity and prevents individual isolation. The interaction between the two factors (ability + multiculturalism) gives the best desired results. In multicultural classes, collaborative learning should be supported by a multicultural education program, otherwise it would have little if any positive effect on the students achievement and attitudes towards learning science. (Contains 21 tables and 1 figure.) [Abstract modified to meet ERIC guidelines.]

Collaboration in the classroom was studied using the Web of Intersubjectivity (L. Albert and others, 1999), a model of collaboration, and the concept of intersubjective inquiry was developed and explored. This study represents the third phase of an ongoing research project. In the first phase, researchers identified the Web of Intersubjectivity and intersubjective inquiry. In the second phase, researchers applied the Web of Intersubjectivity in their professional practices, and phase 3 concerned the use of intersubjective inquiry as research tool to assess the Web of Intersubjectivity as a model to guide the development and support of collaborative groups in individual classrooms. Intersubjective inquiry is a combination of the components of a bootstrap group, as identified by Heron (1996) and the tenets of educational action research. Data sources for this study included recorded conversations, e-mail discussions, field notes, readings, and samples of student work. Pedagogical practices were studied with 84 college algebra students and reflections were studied with 7 graduate students in a Master's program in occupational therapy. Study findings support the use of the Web of Intersubjectivity as a metaphor for planning and modifying collaborative learning experiences at graduate and undergraduate levels. They also show the fluid nature of the Web construct and the usefulness of intersubjective inquiry. (Contains 41 references.) (SLD)

In this paper, an application of one aspect of the Internet technology, namely Internet groups, into the teaching of American and British literatures is evaluated by means of a content analysis of the Internet group which was used as the course component. The aim of this paper was to see how students used the Internet group in the learning of literature so that the effect of the use of an Internet group on learning literature can be discussed. In this study, content analysis was used to understand the nature of the activity situated in the Internet group. The results of the analysis of the Internet group suggest that the Internet group was mainly used by the course takers for formal interaction. It is seen that very little collaboration or real interaction took place amongst the course takers towards creating meaning together. The results support the view that using technology in literature learning has advantages mainly because the students were interested in working in this group although they were neither graded in the activity nor were mandated to take part in it. (Contains 1 figure.) [Abstract and Extended Summary also in Turkish.]

Continuing conflicts between violent groups and states generate an ever-present demand for higher quality and more timely information to support operations to combat terrorism. In particular, better ways are needed to understand how terrorist and insurgent groups adapt over time into more-effective organizations and increasingly dangerous threats. To adapt, terrorist organizations must learn. A group's ability to learn determines its chance of success, since learning is the link between what the group wants to do and its ability to gather the needed information and resources to actually do it. Despite the importance of terrorist group learning, comparatively little focused research effort has been directed at understanding this process and identifying the factors that influence group learning ability. While relevant data and insights can be found in the literature on terrorism and terrorist organizations, this information has not been collected and systematically analyzed to assess its importance from the perspective of efforts to combat terrorism. This study addresses that need in an effort to both analyze current understanding and stimulate further study and research in this area. The RAND Corporation conducted an analysis of organizational learning in terrorist groups and assessed its implications for efforts to combat terrorism. The work was performed between November 2003 and November 2004, a period during which the threat of international terrorism was high and concern about the capabilities of terrorist organizations and how they might change over time was a central focus of policy debate and U.S. homeland security planning. The study is described in this report and in Aptitude for Destruction, Volume 1: Organizational Learning in Terrorist Groups and Its Implications for Combating Terrorism, which applies the analytical framework described in the second part of this report to the practical demands of intelligence and law enforcement activities.