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This paper discusses research on activity structure design in a project-based science classroom and efforts to adapt designs from this setting to an after-school program involving historical inquiry. Common activity structures such as classroom lessons and Initiation-Reply-Evaluation (I-R-E) sequences are important cultural tools that help students and teachers accomplish everyday activities but they are not well adapted to open-ended projects. This paper demonstrates how alternative activity structures scaffold children's performance of complex, open-ended projects. For the activity setting of a project-based earth science class, activity structures at two different time scales support students in accomplishing scientific research. On a more long-term scale, a milestone activity structure works by laying out a small set of interim material artifacts that students turn in over a period of weeks, each of which feeds into portions of the artifact that is the final product and milestone--a science research report. On a shorter time scale, the repetition of Bid-Negotiation-Implementation-Evaluation (B-N-I-E) and Question-Clarification many times helps the teacher guide students in their work while requiring that students remain active. For the activity setting of an after-school history-web club, adapting the milestone activity structure across disciplines presents a number of hurdles because it depends on a discipline-specific task analysis, but the B-N-I-E discourse sequence seems generally adaptable to a wide range of inquiry-oriented work. Contains 30 references. (Author/PVD)

The first purpose of this study was to analyze the results of the confirmatory factor analyses, via EQS, with regard to the latent structures of the Learning and Study Strategies Inventory (LASSI) (C. Weinstein, D. Palmer, and A. Schulte, 1987) as proposed by S. Olejnik and S. Nist (1992), A. Olivarez and M. Tallent-Runnels (1994), B. Olaussen and I. Braten (1998), and the model that evolved from the exploratory factor analysis of the current data set. Data were from two samples, one of 319 college freshmen and the other of 2,535 college freshmen. The aim was not only to determine the model that best fits the data set, but also to investigate how replicable these models are with a large sample size. The model proposed by Olaussen and Braten appears to have the best fit to the data of this study. The two-factor model that evolved from the exploratory factor analysis of the data did not adequately explain the data under the scrutiny of confirmatory factor analysis. This study supports the three latent structure model of the LASSI instrument. The second purpose of the study was to examine the reliability (internal consistency) of the 10 LASSI subscales. The results show that, in some subscales, the Cronbach alpha obtained from this study appear to be slightly better than the indices reported in the manual. Overall, the alpha coefficients obtained in this study reasonably matched those reported in the LASSI manual. (Contains 2 tables, 4 figures, and 16 references.) (Author/SLD)

This study investigated the relationship between a multiple team structure and student achievement in a high minority (predominantly African American), low socioeconomic status middle school over 3 years. Part of a larger 5-year investigation of team structures in high performing restructured middle schools, the study observed the components and interactions of the executive team, leadership team, grade level team, cross-content teams, and interdisciplinary and study teams, noting their influence on student learning outcomes. The analysis took into account the extent of administrative and district support. Data sources included questionnaires, stories teachers wrote about pictures, team meeting observations, classroom observations, interviews, school documents, and state report card data. Data sources provided information related to district and administrative support, multiple teams, teacher integration of multiple team tasks and team learning, classroom practice, and student performance. Results indicated that the systematic alignment of district and administrative directives coupled with multiple teams at the school site and with teacher integration and learning in study teams resulted in changed classroom practices and increased student performance. (Contains 18 references.) (SM)

The purpose of this paper is to discuss the activities of collective learning structures. Drawing from ecological-complexity theory it elaborates on a theoretical framework for observing and acting upon collective learning. To interpret the relation between individual action, social interaction and the collective cognitive domain we adopt the thermodynamic notion of energy-rich matter and the enactivist notion of inter-objectivity. We study the structure, the behavior and the ecology of the collective cognitive domain not only as a catalyst for individual learning, but as a learning body in itself, that emerges from the actions and interactions of individual learning agents. (Contains 1 figure.) [For complete proceedings, see ED500858.]

Three treatments were conducted with 57 computer-assisted design students: students worked individually as instructors presented new commands, a second group worked cooperatively, and a third combined individual and cooperative approaches. No significant differences were found in achievement or attitudes. Cooperative learning may enable more efficient use of computers. Its success may be linked to students' level of grade motivation. (SK)

Three treatments were conducted with 57 computer-assisted design students: students worked individually as instructors presented new commands, a second group worked cooperatively, and a third combined individual and cooperative approaches. No significant differences were found in achievement or attitudes. Cooperative learning may enable more efficient use of computers. Its success may be linked to students' level of grade motivation. (SK)

Knowledge of prerequisite dependencies is crucial to several aspects of learning, from the organization of learning content to the selection of personalized remediation or enrichment for each learner. As the amount of content is scaled up, however, it becomes increasingly difficult to manually specify all of the prerequisites among the different content parts, necessitating automation. Since existing approaches to automatically inferring prerequisite dependencies rely on analysis of content (e.g., topic modeling of text) or performance (e.g., quiz results tied to content) data, they are not feasible in cases where courses have no assessments or only short content pieces (e.g., short video segments). In this paper, we propose an algorithm that extracts prerequisite information using learner behavioral data instead of content and performance data, and apply it to an online short course. By modeling learner interaction with course content through a recurrent neural network-based architecture, our algorithm characterizes the prerequisite structure as latent variables, and estimates them from learner behavior. Through evaluation on a dataset of roughly 12,000 learners in a course we hosted on our platform, we show that our algorithm excels at both predicting behavior and revealing fine-granular insights into prerequisite dependencies between content segments, with validation provided by a course administrator. Our approach of content analytics using large-scale behavioral data complements existing approaches that focus on course content and/or performance data. [For the full proceedings, see ED593090.]

The purpose of this study was to examine two different models of internship and competitively evaluate their effectiveness in influencing interns' experience, beliefs, and knowledge of supervision. The research questions for this study were developed from the literature on supervision of instruction and internships in educational leadership preparation programs. This quasi-experimental study compared the internship experiences, beliefs, and knowledge of two groups of educational leadership students with different supervision assignments: The pre-service teachers group was assigned a pre-service teacher to supervise during the educational leadership internship. The in-service teachers group had an internship that did not include supervising a pre-service teacher; rather it involved supervising teachers at their school. The following research questions were addressed during this study: (1) Does the internship structure affect the quality of supervision practiced in the internship? (2) Does the internship structure affect beliefs about supervision? and (3) Does the internship structure affect knowledge of the supervision process?

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global constraint-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Finally, we also extend our approach by introducing a multi-structure scheme, which learns a set of local correspondence structures to capture the spatial correspondence sub-patterns between a camera pair, so as to handle the spatial misalignments between individual images in a more precise way. Experimental results on various datasets demonstrate the effectiveness of our approach.

For their 61st annual conference, the Association of Literacy Educators and Researchers (ALER) met in St. Petersburg, Florida. This year's conference theme was "Engaging All Readers through Explorations of Literacy, Language, and Culture," which was also used as the title for this year's "Yearbook." This "Yearbook" contains articles about innovative strategies for teachers and teacher leaders, research trends within teacher education programs, and innovative and seminal strategies for preservice teachers. Included are double-peer reviewed papers, the presidential address, and the keynote address: (1) Systems Without a Process: Know What To Do and Then Do It Well (David D. Paige); (2) Reclaiming Creative Literacy Practices (Thomas W. Bean); (3) Understanding Reasoning and Academic Language in Economics (Chang Yuan); (4) "There Might Be Words on the Screen, but it's Not the Same": Engaging Tweens in Literacy through Minecraft Book Groups (Carolyn Stufft); (5) Stranger than Nonfiction: Identifying and Modeling Text Structures in Fiction Picturebooks (Tracey S. Hodges and Sharon D. Matthews); (6) "Es Lo Que Hacemos"/It's What We Do: Translanguaging to Make Meaning During Read Alouds (Erin Greeter); (7) Research on Reader's Theater (Faida Stokes and Chase Young); (8) What DO they do all day?: Field Experiences in Literacy Coaching (Katherine Brodeur, Susan Massey, Lisa Ortmann, and Cynthia Bertelsen); (9) The Engaged Learning Model: The Impact of Digital Literacy and Problem-Based Learning on Fourth Graders' Vocabulary and General Reading Outcomes (James E. Gentry, Chris Sloan, and Robin Pate); (10) Teachers Implementing Coaching Mindsets: A Path to Literacy Leadership (Aimee Morewood, Susan Taylor, Julie Ankrum, Allison Swan Dagen, and Angela Curfman); (11) High Stakes Testing in Teacher Education: Responses and Implications (Maryann Mraz, Bob Rickelman, S. Michael Putman, Paola Pilonieta, and Jean Vintinner); (12) The Lasting Impact of a University Common Reading Program (Kristen Ferguson, Natalya Brown, and Linda Piper); (13) Honoring All of the Language Arts: A Constructivist Approach to Preservice Teachers' Classroom Observations (Delane Bender-Slack and Teresa Young); (14) Preservice Teachers' Sense of Efficacy for Literacy Instruction: Examination of an Undergraduate Delivery System (Corinne Valadez, Tomas Espinosa, Johnathan Hill, and Maureen Sullivan); (15) Cohort Participation: Impact on Personal Learning Networks (Caryssa K. Retrum, Elizabeth Alderton, Glady Van Harpen, and Stephanie Bernander); (16) Disrupting the University Curriculum: Engaging Preservice Teachers in a Study Abroad "Passport" Activity (Virginia R. Massaro, Joan A. Rhodes, and Sharon Zumbrunn); (17) "If You're Not Reading, You're Not Growing": Exploring the Reading Habits of Preservice Teachers in a Children's Literature Course (Bethanie Pletcher and Tomas Espinosa); (18) Shaping and Being Shaped: Exploring Preservice Teachers' Literacy Autobiographies (Stacey J. Korson and Connie Beckner Hodge); (19) Supporting Literacy Teaching Through Rehearsal (Nicholas Husbye); (20) Phonics and Phonemic Awareness in a Teacher Preparation Program: An Analysis of the Achievement and Perceptions of Pre-Service Teachers (Stacey Bose); (21) The Power of Family Involvement: Preparing Preservice Teachers to Understand More about the Out of School Literacy Lives and Cultures of Children (Kathryn V. Dixon, Martha M. Foote, and Juan J. Araujo with Rhonda Clark, Carol Revelle, and Tami Morton); (22) Supporting the Literacy Development of At-Risk First Time College Students through the Exploration of Language and Culture (Connie Briggs, Patricia A. Watson, and Ivan Dole); and (23) Exploring the Reading Strategy Use of EFL College Students: Tri-lingual Chinese Students Attending a University in Korea (Kay Hong-Nam and Susan Szabo). [For the Thirty-Ninth Yearbook, see ED594524.]

The current project aims to investigate the association between an individual’s pattern separation abilities with their “Graph maze” task performance. Pattern separation is the ability to keep similar or overlapping memories separate in your mind, such as where you parked your car today vs where you parked your car yesterday. Participants, aged 18-35, who passed quality control and were included in the initial preliminary analysis of the Graph Maze Task will be reinvited to participate in a follow up study that will include the Mnemonic Similarity Task (MST), which has been shown to be an effective means of characterizing an individual’s pattern separation abilities. Our initial experiments found that there were differences in graph learning performance based on the structure of the graph. There were also large individual differences in performance overall (Kapogianis, in preparation). Given that each graph in our initial study has a unique structure and node organizational pattern, each stimuli object group is unique. However, there is a possibility that there are commonalities between object groups since these groups presented are the adjacent nodes to the currently ‘occupied’ node. For example, if node A is adjacent to both nodes B & C, then node A will be presented when participants occupy either node B or C. These commonalities in node objects across presentations increase with more highly interconnected communities. This gives rise to more opportunity for false recall, which can lead to an incorrect representation of state space. Pattern separation could be important in this situation to help keep the common nodes and similar adjacencies distinct from each other. Graph-like representations of more abstract associations have been recently observed throughout the brain. Representations of state space in medial prefrontal cortex (mPFC) represented nodes within a community – a tightly interconnected cluster of nodes – more similarly within communities than between communities1. Entorhinal cortex has also been shown to code for elements of a graph, representing relative edge distances between nodes in an abstract graph2, while hippocampus was shown to code for semantic similarity between nodes for the same information3. Additionally, the orbitofrontal cortex was shown to code for state space of unobservable task-relevant information4, which could be important for goal-directed planning. Prior work indicates that individuals can extract underlying graph structure in more abstract relationships through experience4-7, such as learning which nodes are closely associated. This ability to extract and connect abstract information through experience can most clearly be observed in recent work which presented pairs of items to participants during the Graph Walk Task5. These item pairs were actually disjointed edge-pairs – two nodes with a common edge – from a larger graph which participants were naïve to, presented in a random order. After repeated presentations of these edge-pairs, participants were able to make accurate relative edge-distance estimations, as well as reconstruct the graph itself, even though they were naïve to the graph's underlying structure at the start of the experiment and they never saw the whole graph. These findings show that individuals learn and connect associations from disordered experiences without prompting or knowledge of the underlying structure during latent learning. Importantly, it was found that performance during the Graph Walk Task was associated with an individual’s pattern separation ability8, and that those with lesser pattern separation abilities could overcome performance deficits when stimuli pairs were presented in blocks to be non-overlapping. For example, if node A is adjacent to nodes B & C, the original stimulus presentations would include A-B and A-C pairs. High pattern separators would have less difficulty distinguishing between A-B and A-C pairs during the same block, while low pattern separators would have more difficulty. But by only presenting pair A-B during study block 1 and pair A-C during block 2, these low pattern separators were able to overcome performance deficits. Our preliminary data for the Graph Maze task suggests that elements of a graph’s underlying structure such as size, edge-to-node ratio, and community structure all impact task performance. However, we theorize that another contributing factor may be an individual’s ability to pattern separate between similar stimuli and whether they are focused on local vs global structure of the underlying graph. Incorporating information into its global context may make regular organizational patterns more apparent and reduce reliance upon local pattern separation by using predictable patterns. This would be similar to navigating a city with a grid layout. However, learning based on local structure would require more robust representations of local connections, and be more reliant on pattern separation abilities in situations with low discriminability at the node level. (see attached image for better table view) Random Grid-like Comm-X-bridge Comm-w/bridge Regularity (global) ↓ ↑↑ ↑ ↑ Discrim. (local) ↑ ↑↑ ↓↓ ↓ Table 1: Regularity (global patterns) and discriminability (local patterns) of graphs used in Community Structure experiment. Graphs with higher regularity have a more global organizational pattern to connections. Graphs with high discriminability have less overlap of object groups meaning each group is relatively unique. The Graph Maze Task has a single study block for each graph where participants are exposed to the underlying structure through self-determined, temporally linked presentations of stimuli which represent the connections of their prior choice. Given this setup, we propose alternative hypotheses for each experiment. The results of the present MST study will adjudicate between them. Parameter search experiment: All graphs in this experiment were randomly generated, so there is no designed community structure. Thus, the focus here is on node number and ENR. a. High pattern separators will have greater task accuracy on high ENR graphs than low pattern separators. Low pattern separators will have greater task accuracy on low ENR graphs than high pattern separators (similar to Noh et al., 2023). b. Alternatively, high pattern separators may have greater task accuracy overall. c. Alternatively, we may see no difference across pattern separators on small graphs and see differences in accuracy across high and low pattern separators on larger graphs similar to hypothesis a. d. Additionally, we may see that working memory may play a larger role as ENR rises as the average stimulus group size grows. In this case, people with greater working memory ability would perform better with greater ENR. Community structure experiment: Here we follow the ideas laid out in Table 1 regarding the global regularity and local discriminability. e. If pattern separation is more relevant to regularity in global structure than to local patterns, we anticipate that task accuracy will be correlated with MST score in order of graph regularity (from lowest to highest performance: random, community w/ bridge, community no bridge, grid-like) f. If pattern separation is more relevant to discriminability in local patterns than to global structure, we anticipate that task accuracy will be correlated with MST score in order of graph discriminability (from lowest to highest performance: community no bridge, community w/ bridge, random, grid-like) g. Alternatively, trial edge distance (how far apart the start and target locations are from each other in terms of number of nodes) may be a significant factor for pattern separation and may need to be taken into consideration during analysis. Trial distance may determine whether start and target nodes are within or across communities, which could affect the degree to which global patterns are needed on any given trial. h. Additionally, we may see that working memory may play a greater role in graphs with greater community structure because participants are forced to plan more. Thus, we would expect the greatest relationship between working memory performance in task performance in the community with bridges and communities without bridges graphs. To address these hypotheses we will invite participants who completed the Graph Maze Task, for both our parameter search and community structure experiments, through Prolific to complete the MST, Spatial N-back, and Verbal N-back, also on Prolific. We will run a linear mixed model of MST Lure Discrimination Index scores, N-back Scores, node number, and edge-to-node ratio with Graph Maze task performance to evaluate the relationship between pattern separation ability, graph structure, and task performance, as well as the contributions of working memory. Participants will be invited to participate through Prolific, and will receive 3 weekly reminders following their initial invitation. The study will remain open for invited participants until 1 week after the final reminder or our goal of 80% of invitees complete the task. The parameter search Graph maze task had 208 participants, so we will target 166 participants. The community structure graph maze task had 52 participants, so we will target 42 participants. Graph Maze Task Description: Participants will complete four blocks of an N-alternative forced choice “graph maze” task, each with a unique non-spatial graph. Each block will consist of an exploration phase and a test phase. Exploration: Before each exploration phase participants will be informed that they will be presented with groups of up to three objects that they must memorize for a subsequent memory test. The language of the instructions will be carefully chosen to exclude spatial and navigation language to prevent priming of a spatial/ navigation reference. They will proceed through these object groups – “navigate” the graph whose underlying graph structure they are naïve to – at a self-guided pace until their exploration time ends. To proceed to the next object group, an item must be selected. To encourage novel object selection, participants will be informed that making different selections will result in different groups being presented. The objects displayed on the screen represent the adjacent (connected) Nodes to the currently occupied node. By selecting one object, participants will transition to a new node and the object group presented will be updated. This process repeats itself until the exploration time for that graph ends. Test: During the test phase, participants are asked to select the target item when it appears, by proceeding through the object groups the same way as in the exploration phase. The target item and the previously selected object will be displayed on the screen during the test trials to reduce working memory load. Participants complete a total of 20 trials per graph, each with a 20s trial time. Mnemonic Similarity Task Description: The task contains two phases: encoding and test phases. The encoding phase consists of a cover task which results in incidental encoding where participants judge whether stimuli are “indoor” or “outdoor” items. Immediately following the encoding phase, participants will perform a recognition task during the test phase, where they will judge stimuli as either new, similar, or old. One-third of the images in the test phase are exact repetitions of images presented in the study phase (targets); one-third of the images are new images not previously seen (foils); and one-third of the images are perceptually similar to those seen during the study phase, but not identical (lures). Lure Discrimination Index (LDI) scores are calculated by calculating the difference between the probability of giving a “Similar” response to the lure items minus the probability of giving a “Similar” response to the foils to account for any bias the participant may have in using the “Similar” response overall. N-back Task Description: The N-back is a test of working memory. It asks participants to continuously update task relevant information and relate it to the current stimulus. The task contains three distinct practice sessions of increasing difficulty and one test phase. Participants are shown a series of stimuli, and asked to respond when the current stimulus matches the stimulus N places back. For example in the 1-back block if the participant saw the sequence of letters “T-K-K”, they would not respond at “T’ or first “K” because those do not match stimuli 1 place back. However, they would respond to the second “K” shown, because it matches the stimulus 1 place back. There are 1-back, 2-back, and 3-back practice blocks and participants must have greater than 80% accuracy on the 1-back and 2-back blocks to proceed. Responses are categorized as a hit, correct rejection, false alarm, or miss. The N-back scores are calculated by the proportion of correct responses (hit and correct rejection) to the total number of stimuli presented. N-back scores give us a quantitative measure of someone’s working memory capacity in a task environment which is continually updating. Both verbal (using letters) and spatial (using locations on a 4x4 grid) versions are used. Analysis Plan: Parameter search: We will conduct a Linear Mixed Model (LMM) between LDI scores from the MST, Spatial and Verbal N-back scores, and the proportion of correct trials and path efficiency (excess steps) during the test phase of the Graph Maze Task. In addition to finding the raw correlation values, we will compare the correlation effect sizes to determine whether there are statistically significantly greater correlations in some conditions of the Graph Maze Task (node number and ENR of graphs). Community Structure: We will conduct a Linear Mixed Model (LMM) between LDI scores from the MST, Spatial and Verbal N-back scores, and the proportion of correct trials and path efficiency (excess steps) during the test phase of the Graph Maze Task. In addition to finding the raw correlation values, we will compare the correlation effect sizes to determine whether there are statistically significantly greater correlations in some conditions of the Graph Maze Task (grid-like, random, community with bridges, community without bridges) than in others. Schapiro, A. C., Rogers, T. T., Cordova, N. I., Turk-Browne, N. B. & Botvinick, M. M. Neural representations of events arise from temporal community structure. Nature Neuroscience 2013 16:4 16, 486–492 (2013). Garvert, M. M., Dolan, R. J. & Behrens, T. E. A map of abstract relational knowledge in the human hippocampal-entorhinal cortex. Elife 6, (2017). Zheng, X. Y. et al. Parallel cognitive maps for short-term statistical and long-term semantic relationships in 1 the hippocampal formation Correspondence to. bioRxiv (2023) doi:10.1101/2022.08.29.505742. Schuck, N. W., Cai, M. B., Wilson, R. C. & Niv, Y. Human Orbitofrontal Cortex Represents a Cognitive Map of State Space. Neuron 91, 1402–1412 (2016). Rmus, M., Ritz, H., Hunter, L. E., Bornstein, A. M. & Shenhav, A. Humans can navigate complex graph structures acquired during latent learning. Cognition 225, (2022). Schapiro, A. C., Rogers, T. T., Cordova, N. I., Turk-Browne, N. B. & Botvinick, M. M. Neural representations of events arise from temporal community structure. Nature Neuroscience 2013 16:4 16, 486–492 (2013). Kahn, A. E., Karuza, E. A., Vettel, J. M. & Bassett, D. S. Network constraints on learnability of probabilistic motor sequences. Nature Human Behavior 936–937 (2018) doi:10.1038/s41562-018-0463-8. Noh, Sharon M., Keiland W. Cooper, Craig Stark, and Aaron Bornstein. Multi-step Inference Can Be Improved Across the Lifespan with Individualized Memory Interventions. PsyArXiv (2023) doi:10.31234/osf.io/3mhj6.

THE REPORT DESCRIBES 13 EXPERIMENTAL STUDIES OF LEARNING IN CHILDREN BETWEEN FOUR AND 12 YEARS OF AGE. THE EXPERIMENTS CONCERN--(1) THE ISOLATION CONDITIONS UNDER WHICH ELABORATIVE FACILITATION OF LEARNING OCCURS, AND (2) THE RELATIONSHIP BETWEEN ELABORATION AND INDIVIDUAL DIFFERENCES IN LEARNING PROFICIENCY. BECAUSE CHILDREN LEARN NOUN PAIRS AND SERIAL LISTS OF NOUNS MORE RAPIDLY WHEN THESE ARE PRESENTED IN A GRAMMATICAL VERBAL CONTENT, SEVERAL EXPLANATORY HYPOTHESES WERE SUBJECTED TO EMPIRICAL TESTS IN THE FIRST STUDY. THE RESULTS INDICATE THAT NOTIONS SUCH AS INTRA-LIST SIMILARITY, SEMANTIC CONSTRAINT, CONTEXT AVAILABILITY, IMPLIED OVERT ACTIVITY, AND VARIATIONS IN FUNCTIONAL STIMULI ARE INADEQUATE. THE REMAINING VIABLE HYPOTHESIS CONCERNS THE UNDERLYING SYNTACTICAL STRUCTURE OF VERBAL CONTESTS. FINALLY, PICTORIAL CONTESTS, ISOMORPHIC WITH THE VERBAL CONTESTS STUDIED, WERE FOUND TO PRODUCE PARALLEL FACILITORY EFFECTS. THE SECOND PROBLEM EXPLORED VARIATIONS IN THE EFFECTS OF ELABORATION AMONG CHILDREN CLASSIFIED BY AGE, SOCIOECONOMIC STATUS (SES), AND INTELLIGENCE. BOTH VERBAL AND PICTORIAL ELABORATION WERE EFFECTIVE ACROSS THE AGE RANGE AND EQUIVALENCE IN LEARNING PROFICIENCY WAS OBSERVED ACROSS SES GROUPS EXCEPT WHEN REPETITION WAS IMPORTANT FOR PERFORMANCE. A TEST WAS DEVELOPED FOR THE PURPOSE OF INDEXING LEARNING ABILITY. INTELLIGENCE TESTS ARE NOT PREDICTIVE OF LEARNING PROFICIENCY AMONG LOW-SES CHILDREN, ALTHOUGH THEY ARE FOR MIDDLE-SES CHILDREN. (AUTHOR)

Project 5 of the GUME study on foreign language teaching methods continues the work of the previous projects concerning the relative effectiveness of a habit-formation (implicit) method and a cognitive-code learning method, with explanations in either the source or target language. The educational problem of Project 5 is teaching the English passive voice to Swedish students in Class 8. The teaching procedures and design differ slightly from the first three projects. The time for explanations varies between the source-language and target-language methods. The source-language method uses comparisons with corresponding structures in the source language where applicable. No significant differences in the learning effects of the three methods are evidenced; interesting trends, however, are noted and discussed. This report presents details on project design, lessons, and evaluation instruments. A statistical description of the experimental population is provided along with a statistical analysis of the results and a correlation study of the variable factors. A discussion of the results and their implications follows. A bibliography is included and appendixes provide details on the English grammatical explanations and on some of the evaluation instruments used. Related documents can be found through the following reference numbers: Project 1, ED 034 172; Project 2, FL 002 818; Project 3, FL 002 819; Project 4, ED 045 969; Adult Project, FL 002 868; statistical synopsis of Projects 1-3, FL 002 816. (VM)

The problem of maximum-likelihood (ML) estimation of discrete tree-structured distributions is considered. Chow and Liu established that ML-estimation reduces to the construction of a maximum-weight spanning tree using the empirical mutual information quantities as the edge weights. Using the theory of large-deviations, we analyze the exponent associated with the error probability of the event that the ML-estimate of the Markov tree structure differs from the true tree structure, given a set of independently drawn samples. By exploiting the fact that the output of ML-estimation is a tree, we establish that the error exponent is equal to the exponential rate of decay of a single dominant crossover event. We prove that in this dominant crossover event, a non-neighbor node pair replaces a true edge of the distribution that is along the path of edges in the true tree graph connecting the nodes in the non-neighbor pair. Using ideas from Euclidean information theory, we then analyze the scenario of ML-estimation in the very noisy learning regime and show that the error exponent can be approximated as a ratio, which is interpreted as the signal-to-noise ratio (SNR) for learning tree distributions. We show via numerical experiments that in this regime, our SNR approximation is accurate.

Directed toward the training needs of distance education systems which are in a mixed stage of development, this three-part manual sets out training modules for an assortment of specialists currently contributing to distance learning systems. The first part discusses basic principles, including the aims of training, who should be trained, what they will be trained in, who will train them, how they will be trained, and where and for how long distance educators will be trained. Tasks and training needs as proposed in a report of an earlier APEID (Asia and the Pacific Programme of Educational Innovation in Development) regional seminar are appended to this section. The second section describes the training modules and how they are used and provides exemplar modules designed for the training of personnel involved in system administration, the production of correspondence materials, or the writing of scripts for educational radio or television. Orientation materials for distance learners and their families are also provided. The third section contains examples of training modules and materials that can be used to supplement the workshop programs, including bulletins, newsletters, journals, conferences, a mid-career training program for distance education personnel, and preservice and broadcast study programs. A glossary of 17 distance education terms concludes the manual. (RP)

This paper describes some game situations used to study how subjects learn mathematical structures, in particular the structures of the cyclic groups of orders 2 and 4 and the Klein-four group. A series of experiments are reviewed and the methods used to determine whether subjects did learn the structures are discussed. Differences in strategies, conceptualizations, and interpretations are presented. Possible effects of such factors as age, sex, ability, and method of presentation are also discussed. (Author/LS)

Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models that are widely used in educational and psychological measurement. A key component of CDMs is the Q-matrix characterizing the dependence structure between the items and the latent attributes. Additionally, researchers also assume in many applications certain hierarchical structures among the latent attributes to characterize their dependence. In most CDM applications, the attribute--attribute hierarchical structures, the item-attribute Q-matrix, the item-level diagnostic models, as well as the number of latent attributes, need to be fully or partially pre-specified, which however may be subjective and misspecified as noted by many recent studies. This paper considers the problem of jointly learning these latent and hierarchical structures in CDMs from observed data with minimal model assumptions. Specifically, a penalized likelihood approach is proposed to select the number of attributes and estimate the latent and hierarchical structures simultaneously. An expectation-maximization (EM) algorithm is developed for efficient computation, and statistical consistency theory is also established under mild conditions. The good performance of the proposed method is illustrated by simulation studies and real data applications in educational assessment.

A study examined the effect of divided and alternating comparison/contrast text structures on the reading comprehension of the college developmental reader. In the divided pattern, all of the information about an object is provided and then all of the information about the other object is provided, while in the alternating pattern comparisons between the two objects are made point by point. Subjects, 24 students ranging in age from 19 to 26 years and classified as less able readers, were given two short and two long reading selections concerning unfamiliar scientific topics. Each subject received two alternating and two divided comparison/contrast structures. Subjects responded to multiple choice questions for each passage. Results indicated that there was no significant difference in the reading comprehension of the different types of text structure. More research is needed, but until that time college reading instructors should consider developing materials that train students to master comparison/contrast patterns across several reading and writing contexts. (Sixteen references, one table of data, one figure, and a master list of College Reading and Learning Assistance Technical Reports are appended.) (RS)

The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

This thesis assessed the Army Distance Learning Plan (ADLP) and the current programs associated with implementing the current Army guidance for distance learning. The thesis set out to determine what requirements went into developing OSs and in doing so surveyed civilian educators and U.S. Army distance learning plan managers to examine the DLPs they were directing. The analysis showed that neither army nor civilian distance learning plan managers used formalized requirements for organizational structure development (OSD). Instead, they developed DLPs and then established OSs that met the needs of the plan. The recommendations to CGSC were to establish the climate to aid DLP success and to determine the OS by identifying the needs of the target populations of student and the design of the DLP. Designing the DLP should only occur after OSD and should he flexible enough to facilitate changes in the size, composition, and needs of the target audience. The OS must power-down DL approaches and train the instructors on DL media options. Last, the college must invest for the longevity of the DLP by hiring key personnel to staff OS and manage the DLP.

This thesis assessed the Army Distance Learning Plan (ADLP) and the current programs associated with implementing the current Army guidance for distance learning. The thesis set out to determine what requirements went into developing OSs and in doing so surveyed civilian educators and U.S. Army distance learning plan managers to examine the DLPs they were directing. The analysis showed that neither army nor civilian distance learning plan managers used formalized requirements for organizational structure development (OSD). Instead, they developed DLPs and then established OSs that met the needs of the plan. The recommendations to CGSC were to establish the climate to aid DLP success and to determine the OS by identifying the needs of the target populations of student and the design of the DLP. Designing the DLP should only occur after OSD and should he flexible enough to facilitate changes in the size, composition, and needs of the target audience. The OS must power-down DL approaches and train the instructors on DL media options. Last, the college must invest for the longevity of the DLP by hiring key personnel to staff OS and manage the DLP.

Patriarchy is a threshold concept in women's studies--a significant, defining concept that transforms students' understanding of the discipline. This article reviews our design, implementation, and findings of a lesson study crafted to teach women's studies students the complex idea of patriarchy as a social system. We analyze the lesson using both quantitative and qualitative analysis, in keeping with the ideals of feminist pedagogy, suggesting a new model of SoTL research for the women's studies field.

Introduction: This paper discusses an "information in context" design project at Auraria Library in Denver, Colorado which aims to collaboratively create organizational structures and communication systems with and for library employees. Method: This action research project is founded within shared leadership, informed learning and organizational learning principles and implemented using participatory design processes. The first phase involved an appreciative inquiry process resulting in an organizational realignment of personnel and the introduction of shared leadership. The second phase involved the co-design of organizational information and communication systems and subsequent implementation of initiatives. Results: This project resulted in several of the co-designed knowledge initiatives being implemented within Auraria Library to enhance communication, decision making and planning systems. These included both face to face and technology enabled initiatives such as such as "brown bag" lunches to a new wiki based intranet system. Conclusions: This project advances professional practice through better understanding how to create workplace contexts that cultivate individual and collective learning through situated 'information in context' experiences. An appreciative framework was developed which values information sharing and enables knowledge creation through shared leadership.

Many recent delineation techniques owe much of their increased effectiveness to path classification algorithms that make it possible to distinguish promising paths from others. The downside of this development is that they require annotated training data, which is tedious to produce. In this paper, we propose an Active Learning approach that considerably speeds up the annotation process. Unlike standard ones, it takes advantage of the specificities of the delineation problem. It operates on a graph and can reduce the training set size by up to 80% without compromising the reconstruction quality. We will show that our approach outperforms conventional ones on various biomedical and natural image datasets, thus showing that it is broadly applicable.

In a unique class of combined elementary and secondary teacher education majors, this triangulated qualitative study investigated how cooperative structures affect student attitudes and motivation. In particular, the study examined students' preferences for instruction, students' attitudes and motivation related to their preference for cooperative structures, and students' perceptions of their own learning. Students were instructed in a lecture or lecture-discussion group using an incremental approach implementing the Jigsaw, Focused Discussion, Turn-to-Your-Partner, and TGT (Team, Games, Tournaments) cooperative structures. Five weeks of typical course content with cooperative groups was provided. Then students received an overview of cooperative learning history, theory, and characteristics of effective use. Providing experience with cooperative activities before introducing cooperative learning as a topic for study led to more positive attitudes. However, responses on attitude and motivation subscales of the "Learning Preference Scale: Students" (LPSS) and the "Learning and Study Strategies Inventory" (LASSI) indicated that students in the study had an above average attitude score and a below average motivation score. These findings were discussed with students in ethnographic interviews on student perceptions of college-level instruction and attitude and motivation toward learning. The study concluded that cooperative learning structures generally yielded positive outcomes for students. A final conclusion was that the students' interest in the material, use of language in learning the material, and active pursuit of learning was enhanced by structured cooperative groups. The appendix describes the cooperative structures used and how to implement them. (Contains 2 tables, 3 figures and 53 references.) (JB)

Protocole d'essai émulé évaluant l'impact de l'enseignement problem based learning sur les résultats aux ECOS facultaires.

Protocole d'essai émulé évaluant l'impact de l'enseignement problem based learning sur les résultats aux ECOS facultaires.

Protocole d'essai émulé évaluant l'impact de l'enseignement problem based learning sur les résultats aux ECOS facultaires.

Strong spoken language skills are fundamental for literacy development when children start school, and for later academic achievement and life outcomes (Adolf & Hogan, 2019). In the current study, we implement usage-based theoretical frameworks (e.g., Ambridge & Lieven, 2015; Rowland et al., 2012) and findings from child language research (e.g., Huttenlocher et al., 2002; Hesketh et al., 2016; Vasilyeva, 2006; Vasilyeva et al., 2008) to assess the extent to which structured language input might enhance children’s language in a multilingual setting. We examine this question in children speaking Kannada, an understudied agglutinating language of South India, used by 70 million speakers. We build on the research that has established a strong role for implicit learning mechanisms and language production in learning syntactic structures (e.g., Chang et al., 2006; Hopman & MacDonald, 2018; Kidd, 2012), and we extend it to the learning of narrative structure (story grammar) (e.g., Silva et al. 2014; Silva & Cain, 2019). We designed a story-based spoken language study with a primary aim to examine the role of repeated exposure to structured narratives and targeted syntactic structures in enhancing their use in a diverse sample of multilingual children where the target language (Kannada) was a first or an additional language. Our secondary aim was to assess the feasibility of this method as an oral language intervention within a school setting.

A follow-up to Episode 17 on Jim Knight's The Partnership Learning Principles, this episode focuses on how to implement those principles using the different learning structures, such as stories and cooperative thinking.

The purpose of this study was to investigate the effects of using individual, cooperative-dyad, and collaborative-dyad learning structures with a computer simulation. College students implemented and experienced one of the three learning structures while using a computer simulation designed to teach the technical and procedural aspects of accounting. Results indicated that performance scores were high, and time on task similar, regardless of learning structure; however, students who worked alone expressed significantly more continuing motivation for their learning structure than students who worked with a partner. Responses to student interviews revealed somewhat mixed feelings for the small group structures. More than 60% of the student overall commented that they would enjoy working with a partner, but less than 50% said they thought they would learn more with a partner. Observation of the small groups indicated that students in the cooperative dyads exhibited significantly more discussion and provided more answers to their partners' questions than students in the collaborative dyads. Implications for implementing small group structures with computer-based instruction are provided, and two tables summarize student responses. (Contains 25 references.) (Author/SWC)

In this research, the number of dowels (horizontal connection) has been determined using support vector machines (SVM), gradient boosting and artificial neural networks (ANN-Multilayer perceptron). Building height, length and thickness of the wall, maximum shear, maximum compressive force and maximum tension were the input parameters while the output parameter was the number of dowels. 1140 machine learning models were used, out of which 814 were used as training datasets and 326 as test datasets. A coefficient of correlation of 0.9264, root mean square error of 0.3677 and scattering Index of 4.75 % was achieved by SVM radial basis kernel function (SVM-RBF) as compared to a coefficient of correlation of 0.9232, root mean square error of 0.3743 and scattering Index of 4.83 % by resilient ANN-Multilayer perceptron, suggesting that SVM-RBF is more accurate in estimating the number of dowels. The study's encouraging findings highlight the need for additional research into the use of machine learning in civil engineering.

Sponsors ? Thinkster.io ? Adventures in Blockchain - Devchat.tv ? My Ruby Story - Devchat.tv ? CacheFly Panel ? Joe Eames ? Brooke Avery ? Jesse Sanders ? Mike Dane ? Sam Julien ? Luis Hernandez Joined by special guest:  Dylan Israel Episode Summary In this episode of the Dev Ed podcast, the panelists talk to Dylan Israel. Dylan is a self-taught software engineer working as a developer and a content creator. His YouTube channel has over 60k subscribers where he aims to help people trying to learn programming on their own. He teaches a course on Thinkster.io called "100 Algorithms Challenge", aimed at developers preparing for technical whiteboard interviews or those wanting regular challenges to improve their skills, and has a collection of the top most commonly asked interview questions on algorithms and data structures. Joe starts the discussion by throwing the fundamental question to the panel - What is the meaning of the terms "data structures" and "algorithms". Dylan explains that these are fairly common concepts in software development, and traditionally data structures represent the way data is organized and algorithms define how to parse through them while maintaining optimum performance using fewer iterations and reduced time. Others chime in as well and explain these terms using the analogy of building blocks and recipes. After the basics, they discuss why should one bother learning data structures and algorithms in the first place. The main reasons mentioned are cracking job interviews especially for high paying jobs at large companies, efficient computing, forcing one to think differently and out of the box, studying time and space complexity leading to a better understanding of the software. Joe mentions that what we learn in a computer science class is rarely used at an actual job, and asks the panel to challenge his statement that learning data structures and algorithms except for clearing job interviews, is inherently a waste of time. Brooke explains that learning about them helps in getting into the right mindset, whereas Dylan says that he has had a chance to use them in certain significant applications on his e-commerce platform, and Jesse adds that they help in honing developer skills to a large extent. Thinking on a level higher than what is expected in order to create efficient solutions, and understanding things well through problem-solving are some of the important takeaways from learning these concepts. The panelists then discuss some great ways to learn data structures and algorithms. They share their own interesting interview experiences offering insight into what worked for each of them, and suggest books, online resources including courses, and emphasize that practicing a ton of problem-solving on a whiteboard/paper is one of the best ways to go about it. They also mention that recognizing repetitive patterns in problems is a good approach, and using a different language to solve can be beneficial too. They also advise listeners to take into consideration the opportunity cost involved in spending a significant amount of time learning data structures and algorithms, so that they can take an informed decision. They talk about how much the knowledge of these concepts affects their hiring decisions and what exactly do they look for in candidates. They wrap up the show by each giving one piece of advice to someone preparing for a job - comparing solutions with others and learning from them, consistent attitude, test-driven development, interviewing a lot and researching about the interview as well as the interviewing panel. They end the episode with picks. Links ? Dylan Israel - YouTube ? 100 Algorithms Challenge ? Cracking the Coding Interview Picks Luis Hernandez: ? The Imposter's Handbook Combo Brooke Avery: ? 10 Day Algorithm Challenge ? The Art of Racing in the Rain Dylan Israel: ? Pramp Mike Dane: ? Google Fi Sam Julien: ? Base CS podcast ? You need a budget Jesse Sanders: ? CSS Tricks ? Hawaii Joe Eames: ? Stuff Matters: Exploring the Marvelous Materials That Shape Our Man-Made World

Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the structure of independences of a domain, more accurate joint probability distributions can be obtained for inference tasks or, more directly, for interpreting the most significant relations among the variables. Recently, several researchers have investigated techniques for automatically learning the structure from data by obtaining the probabilistic maximum-a-posteriori structure given the available data. However, all the approximations proposed decompose the posterior of the whole structure into local sub-problems, by assuming that the posteriors of the Markov blankets of all the variables are mutually independent. In this work, we propose a scoring function for relaxing such assumption. The Blankets Joint Posterior score computes the joint posterior of structures as a joint distribution of the collection of its Markov blankets. Essentially, the whole posterior is obtained by computing the posterior of the blanket of each variable as a conditional distribution that takes into account information from other blankets in the network. We show in our experimental results that the proposed approximation can improve the sample complexity of state-of-the-art scores when learning complex networks, where the independence assumption between blanket variables is clearly incorrect.

Peer grading is the process of students reviewing each others' work, such as homework submissions, and has lately become a popular mechanism used in massive open online courses (MOOCs). Intrigued by this idea, we used it in a course on algorithms and data structures at the University of Hamburg. Throughout the whole semester, students repeatedly handed in submissions to exercises, which were then evaluated both by teaching assistants and by a peer grading mechanism, yielding a large dataset of teacher and peer grades. We applied different statistical and machine learning methods to aggregate the peer grades in order to come up with accurate final grades for the submissions (supervised and unsupervised, methods based on numeric scores and ordinal rankings). Surprisingly, none of them improves over the baseline of using the mean peer grade as the final grade. We discuss a number of possible explanations for these results and present a thorough analysis of the generated dataset.

Leveraging the development of artificial intelligence, in recent years several medical sectors have integrated automated solutions to segment anatomical structures from bioimaging with deep learning approaches. Musculoskeletal System segmentation is key for studying anatomical tissue alterations and supporting medical interventions. The clinical usage of such tools requires understanding the proper way to read their results and evaluate their performance. The current systematic review aims at presenting the common bottlenecks for musculoskeletal structures analysis (e.g., small sample size, data inhomogeneity) and strategies faced by different Authors for similar tool development. Research has been performed on the PUBMED database with the following keywords: deep learning, musculoskeletal system, segmentation. A total of 140 articles published until February 2022 were obtained as query output and analyzed according to PRISMA framework in terms of: anatomical structures, bioimaging techniques, pre/post-processing operations, training/validation/testing subset creations, network architectures, loss functions, performance indicators and so on. Even if from this survey emerged that there are some common trends, the comparison between different methods needs to be discussed based on each specific case-study (anatomical region, medical imaging acquisition setting, study population, etc.). These findings can guide clinicians (as end users) to better understand the potential benefits and limitations of these tools.

Reproducibility review completed, adding a registration for proper archival and snapshot.

This study aims at testing whether prior knowledge affects knowledge structuring in category learning, and thereby the ease of learning specific category structures. In particular, the central goal is to test a qualitative prediction of the novel CAL model (Schlegelmilch, Wills, & von Helversen, in press) that learning a Type II (or disjunctive) category structure (Shepard, Hovland, & Jenkins, 1961) is facilitated if a Type I (or simple rule) structure is learned beforehand, if the dimension that is relevant for solving Type I remains relevant in the Type II problem (congruent condition), compared to a condition in which the Type I rule dimension becomes irrelevant in Type II (incongruent condition). Theoretically, thus, the prior knowledge of a rule is methodologically induced by learning a Type I rule (e.g., black objects belong to category A, white objects to category B), which from a CAL perspective, can or can not be modulated in subsequent Type II learning (e.g., congruent: black objects belong to category A, white objects to category B, for large, but not for small objects; incongruent: square objects belong to category A, circle objects to category B, for large, but not for small objects). The pre-registered experiment is a follow-up on an earlier (conceptually identical study; with N(congruent)=24, and N(incongruent)=28) in which we found first evidence that supports the basic prediction of CAL. The purpose of the pre-registered experiment is to replicate the finding, and to address open questions regarding the finer details of the empirical and predicted patterns with a larger sample size. In particular, in the previous study we found that Type II learning performance was higher in the congruent compared to the incongruent condition across the whole learning phase (i.e., the difference in average learning curves was quickly increasing and relatively stable across the 12 training blocks). This only partially supports the CAL hypothesis (in its current implementation) as it predicts an advantage in the congruent condition only in about the first few blocks of the Type II learning phase diminishing in the remaining trials. In this regard, we aim to address the following questions. (1) On closer inspection, there seems to be a possible explanation in CAL of the prolonged Type II advantage in the previous experiment. That is, we observed lower and more individually diverse performances already in the first block of Type I learning in the incongruent condition, than in the congruent condition. This means, it is possible that participants differed in their rule-learning ability (or strategy) between both groups (due to random fluctuations and small sample size), which also should affect Type II learning, thereby giving rise to the overall pattern. In fact, in simulations CAL would predict both (more diverse Type I and slower Type II learning in the incongruent condition), when decreasing its gamma parameter (i.e., the overall degree of contrasting in CAL) relative to a simulation for the congruent condition. By collecting a larger sample, we seek to exclude these random fluctuations and test the Type II differences in a more reliable sample. (2) Furthermore, in model simulations we found that the strength of the predicted Type II advantage in the first trials of the Type II learning phase is enhanced with stronger contrasting (CAL's gamma parameter). In our previous work, we have shown that rule instructions affect learning consistent with the hypothesis of increased strength of contrasting. Thus, to further homogenize the sample and to facilitate finding the predicted effect (and in contrast to the previous experiment), we will instruct participants to learn categorization rules. (3) Furthermore, even without such additional hypotheses, we seek to explore some of CAL’s implementational assumptions that could influence the time horizon of the predicted Type II advantage. For instance, the technical implementation of modulation learning or the application of independent learning parameters on CAL's learning functions (e.g., modulation generalization and rule generalization). (4) A specific second prediction that differentiates CAL from existing other models is that Type II performance is generally non-normally distributed between individuals. That is, CAL predicts that this bi-modality diminishes only later in the incongruent condition but earlier in the congruent condition, driving the predicted Type II advantage. Hence, based on Kurtz et al. (2013), we argue that this phenomenon can be even observed early in learning and with rule instructions. In particular, the general prediction is, that participants completely solve Type II very suddenly, which, however, happens either very early or only later depending on individual differences in contrasting (captured by CAL's gamma parameter). Thus, Type II performance is predicted to be bi-modal (at least) in the first blocks of Type II learning, which we seek to test empirically. If we find the same Type II patterns as in the previous study, even when random fluctuations can be reduced with larger sample sizes and instructions to learn rules, CAL's model implementation needs to be revisited to account for the effects of our manipulation. In particular, it is generally unknown how participants specifically recognize or react to changing category structures if this change is not indicated by explicit external triggers (e.g., peripheral cues) in the exact moment of switching the structures. This raises theoretical questions about what happens if a perfect Type I rule is learned (e.g., further use or forgetting of modulators), and/or if the learned Type I rule turns out to be invalid (i.e., which representations are maintained, and which are reset, and what are the internal/subjective indicators of such changes in the outside world).

Comprehension of a problem or task that is generated in the real world rather than presented as a well-defined problem-statement of the kind encountered in textbooks or psychological laboratories was related to the ability of recognizing, selecting and formulating problems. The process of acquiring and utilizing this ability was conceptualized with the help of flow diagrams for algorithms. This resulted in the furthering of a new and fruitful theory of cognitive learning which stresses the formation and use of hypothesis and how to represent them. New experimental techniques were developed for measuring performance and quality of questions, on problems requiring shifts in representation. These were applied to investigate the effect of experience in learning to formulate such problems in fifth graders and in college students. New procedures for exposing learners to such experiences were also derived and tested. Results suggest that children learn problem recognition and formulation if they are exposed to inquiry-provoking situations where they have to form hypotheses. College students with experience in having to shift representations perform better on tasks requiring such shifts than those who don't. Question-quality was demonstrated to be correlated with problem-solving performance. (Author)

Comprehension of a problem or task that is generated in the real world rather than presented as a well-defined problem-statement of the kind encountered in textbooks or psychological laboratories was related to the ability of recognizing, selecting and formulating problems. The process of acquiring and utilizing this ability was conceptualized with the help of flow diagrams for algorithms. This resulted in the furthering of a new and fruitful theory of cognitive learning which stresses the formation and use of hypothesis and how to represent them. New experimental techniques were developed for measuring performance and quality of questions, on problems requiring shifts in representation. These were applied to investigate the effect of experience in learning to formulate such problems in fifth graders and in college students. New procedures for exposing learners to such experiences were also derived and tested. Results suggest that children learn problem recognition and formulation if they are exposed to inquiry-provoking situations where they have to form hypotheses. College students with experience in having to shift representations perform better on tasks requiring such shifts than those who don't. Question-quality was demonstrated to be correlated with problem-solving performance. (Author)

This paper presents an initial analysis of 10 Preschool children's responses to a look-draw-trace-draw task. The findings suggest that figure-tracing helped half the children to produce a more accurate representation of the geometric figure presented to them, in their second drawing.

Twenty-two elementary and middle school teachers were studied over the course of a school year as they implemented cooperative learning structures in their classrooms. Twelve of the teachers did peer coaching and 10 did not. The teachers participated in 2 days of staff development to learn six specific cooperative structures and to learn and practice a method of peer coaching. Qualitative data-gathering and analysis methods were used. Content analysis of both oral and written discourses investigated the process of peer coaching in the implementation of cooperative learning structures. Results indicated that the process of peer coaching did affect the implementation of cooperative learning structures. Peer coaching appeared to encourage teachers to reflect on their teaching, to take risks, and to change. Of the teachers who participated in the original staff development on cooperative learning, only eight used the new structures at a level of use which focused on the students rather than on the teacher; all of these teachers had participated in the peer coaching, indicating that the coaching process facilitated the successful transfer of a new strategy from the workshop environment to the classroom. (Contains 31 references.) (JB)

Twenty-two elementary and middle school teachers were studied over the course of a school year as they implemented cooperative learning structures in their classrooms. Twelve of the teachers did peer coaching and 10 did not. The teachers participated in 2 days of staff development to learn six specific cooperative structures and to learn and practice a method of peer coaching. Qualitative data-gathering and analysis methods were used. Content analysis of both oral and written discourses investigated the process of peer coaching in the implementation of cooperative learning structures. Results indicated that the process of peer coaching did affect the implementation of cooperative learning structures. Peer coaching appeared to encourage teachers to reflect on their teaching, to take risks, and to change. Of the teachers who participated in the original staff development on cooperative learning, only eight used the new structures at a level of use which focused on the students rather than on the teacher; all of these teachers had participated in the peer coaching, indicating that the coaching process facilitated the successful transfer of a new strategy from the workshop environment to the classroom. (Contains 31 references.) (JB)

We propose a novel approach to identify one of the most significant dermoscopic criteria in the diagnosis of Cutaneous Melanoma: the Blue-whitish structure. In this paper, we achieve this goal in a Multiple Instance Learning framework using only image-level labels of whether the feature is present or not. As the output, we predict the image classification label and as well localize the feature in the image. Experiments are conducted on a challenging dataset with results outperforming state-of-the-art. This study provides an improvement on the scope of modelling for computerized image analysis of skin lesions, in particular in that it puts forward a framework for identification of dermoscopic local features from weakly-labelled data.

The ability to extract regularities from the environment, known as statistical learning (SL), facilitates the processing of complex stimuli. At the same time, metamemory studies have shown that people’s assessments of their own learning and memory – judgments of learning (JOLs) – are based on cues that pertain to the study materials (e.g., word frequency), study conditions (e.g., number of study opportunities) or that signal ease during the learning process (e.g., fluency). However, whether linguistic structures acquired via SL are used as cues to inform JOLs has not yet been addressed. In this study, we will test the impact of regularities in the co-occurrences of syllables within artificial words on JOLs. N = 90 participants will be exposed to a continuous auditory stream of artificial words with fixed transitional probabilities between adjacent syllables during a familiarization phase. Afterwards, they will study and make JOLs for items that follow the transitional probabilities and were presented in the familiarization phase (“word” item type), for items that follow the transitional probabilities but were not presented in the familiarization phase (“phantom” item type) and for items that do not follow the transitional probabilities and were not presented in the familiarization phase (“non-word” item type). To assess SL, one group of participants “SL-and-JOLs” (n = 45) will be asked whether the item belongs to the language from the auditory stream or not before making each JOL. To test SL influences on metamemory without prompting participants to make SL classifications, the other group of participants “JOLs-only” (n = 45) will be asked to make JOLs only. Finally, participants will complete a recognition memory task. We expect that if SL occurs, the proportion of items classified as words belonging to the language from the auditory stream will be higher for “word” and “phantom” items than for “non-word” items (SL-and-JOLs group only). Further, if SL occurs and the linguistic regularities acquired via SL inform JOLs, we expect that JOLs will be higher for “word” and “phantom” items than for “non-word” items in both groups. This study will shed light on whether cues for metamemory judgments can be learned through the extraction of regularities from the environment. This would indicate that SL does not only facilitate learning at an object-level, but that people also base their metamemory on its facilitating effects.

A theoretical orientation to three goal structures (cooperation, competition, and individualization) is postulated, based upon Deutsch's extension of Lewin's theory of motivation. Lewin postulated that a state of tension within a person motivates movement toward the accomplishment of desired goals; a goal structure specifies the type of interdependence existing among persons as they work towards goal accomplishment. Discussion focuses on the conditions under which each goal structure may be productively used in instruction, the research relating to the overuse and inappropriate use of competition, and the research on the effects of cooperative goal structure on the coutcomes of instruction. Briefly summarized are the findings of the research now available in social psychology and education on the effects of goal structures on education. The results of the research overwhelmingly point to the need to train teachers in the procedures for all goal structures appropriately. (CS)

Item responses of two samples each of 622 normal and 573 educable mentally retarded children, 6 to 15 years of age, on Raven's Colored Progressive Matrices were submitted to a principal components analysis and varimax rotation. The following four factors were obtained: continuity and reconstruction of simple and complex structures, discrete pattern completion, reasoning by analogy, and simple continuous pattern completion. The factors corresponded to readily identifiable problem types. Comparability of factor structures of normal and retarded subjects indicated the factorial invariance of the test with children of different IQ levels. (Author)

Item responses of two samples each of 622 normal and 573 educable mentally retarded children, 6 to 15 years of age, on Raven's Colored Progressive Matrices were submitted to a principal components analysis and varimax rotation. The following four factors were obtained: continuity and reconstruction of simple and complex structures, discrete pattern completion, reasoning by analogy, and simple continuous pattern completion. The factors corresponded to readily identifiable problem types. Comparability of factor structures of normal and retarded subjects indicated the factorial invariance of the test with children of different IQ levels. (Author)

English-Language Learners (ELLs, English-Learners, ELs) are a particularly challenging sector of the student population in United States schools. They constitute an increasingly larger presence in most school districts, growing 51 percent in ten years to 5.1 million in 2006. Despite becoming more common, schools have yet to figure out ways to meet the needs of these students, who continue to lag behind in most academic achievement measures. Although "English-Language-Learner" is an important demographic category, the designation is problematic as a reference point for teaching practice among teachers and teacher educators. In this article the author argues for a shift in the definition of teaching practice for teachers and teacher educators away from "English learners" toward "language use for academic purposes" as a perspective from which to examine their practice. This self-study is an instance of a teacher educator interested in experiential, hands-on pedagogy to foster critical language awareness among preservice teachers. The author begins by proposing a re-conceptualization of teaching and teacher preparation in terms of learning outcomes, not student types. He argues that "English-Language-Learners" is (a) both too broad and not inclusive enough, (b) likely to elicit views of students as deficient, (c) not conducive to "one-size-fits-all" approaches (Reyes, 1992), and (d) lacking a widely-accepted theory or model to explain the relationship between teaching and learning. He then analyzes his students' developing awareness of academic language after completing two PLTs, one in English, the other in Spanish. After discussing findings, the author closes with conclusions and recommendations regarding academic language use as another form of pedagogical content knowledge. (Contains 1 table and 3 notes.)