Asterhan, C. S. C., & Lefstein, A. (2020).
Evidence-based Design Principles for Effective Professional Development: A Critical Appraisal of the Evidence.
Proceedings of the 14th International Conference of the Learning Sciences (pp. 2046-2052) . International Society of the Learning Sciences.
AbstractScholarly efforts to identify core design features for effective professional development (PD) efforts have rapidly grown in the last two decades. Based on extensive literature reviews, meta analyses and large-scale quantitative studies, scholars have arrived at short lists of core design principles for effective PD programs. These design principles are presented as based on strong evidence from large-scale, replicated and rigorous research studies, and as at the heart of consensus among PD scholars. In the present essay, we appraise the quality of the evidence on which this claim is based. We identify several major flaws in the research base on which such claims are based and conclude that, overall, the evidence is weak and claims about strong evidence-based findings are misleading. Additional reservations about this research program are discussed.
pdf Babichenko, M., Asterhan, C. S. C., & Lefstein, A. (2020).
Inquiry Into Practice in School-Based Teacher Team Activities: Comparing Video Analysis, Peer Consultation and Pedagogical Planning. Proceedings of the 14th International Conference of the Learning Sciences (pp. 1966-1973) . International Society of the Learning Sciences.
AbstractThis study contributes to growing scholarly interest in teacher-led, on-the-job learning communities and how collaborative inquiry into practice can be supported in such
contexts. We particularly focus on the relative advantages and limitations of three teacher team activity types: video analysis, peer consultation and pedagogical planning. Fifty-four
transcribed teacher meeting excerpts were analyzed using the CLIP coding scheme for teacher collaborative inquiry into practice, assessing aspects of inquiry-based reasoning, collaboration and focus on pedagogy (teacher actions, student thinking and disciplinary content). Quantitative comparisons showed that, overall, collaborative inquiry into practice was lowest during peer consultations, in part because teachers were often not positioned as agents of change in such conversations. Teachers tended to inquire into each other’s ideas more often during pedagogical planning. Surprisingly, teacher team video analysis activities were not characterized by higher measures of attention to student thinking, nor inquiry orientation. Practical and theoretical implications are discussed.
pdf Asterhan, C. S. C., & Lefstein, A. (2020).
Teacher Professional Development: Structures, Strategies, Principles and Effectiveness (in Hebrew). In
M. Mikulincer & R. Parzanchevsky-Amir (Ed.),
Optimal management of professional development and training in the education system – Status report and recommendations (pp. 44-53) . Jerusalem: Yozma – Centre for Knowledge and Research in Education, The Israel Academy of Sciences and Humanities.
Publisher's Version Ophir, Y., Tikochinski, R., Asterhan, C. S. C., Sisso, I., & Reichart, R. (2020).
Deep neural network models detect suicide risk from textual Facebook postings.
Nature Scientific Reports ,
10, 16685.
Publisher's VersionAbstractDetection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56 – 0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social media users. The dataset included 83,292 postings authored by 1,002 authenticated Facebook users, alongside valid psychosocial information about the users. Using Deep Contextualized Word Embeddings for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (.621 ≤ AUC ≤ .629), the MTM produced significantly improved prediction accuracy (.697 ≤ AUC ≤ .746), with substantially larger effect sizes (.729 ≤ d ≤ .936). Subsequent content analyses suggested that predictions did not rely on explicit suicide-related themes, but on a range of text features. The findings suggest that machine learning based analyses of everyday social media activity can improve suicide risk predictions and contribute to the development of practical detection tools.
Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56 – 0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social media users. The dataset included 83,292 postings authored by 1,002 authenticated Facebook users, alongside clinically valid psychosocial information about the users. Using Deep Contextualized Word Embeddings for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (.606 621 ≤ AUC ≤ .608629), the MTM produced significantly improved prediction accuracy (.690 697 ≤ AUC ≤ .759746), with substantially larger effect sizes (.701 729 ≤ d ≤ .994936). Subsequent content analyses suggested that predictions did not rely on explicit suicide-related themes, but on a range of text features. The findings suggest that machine learning based analyses of everyday social media activity can improve suicide risk predictions and contribute to the development of practical detection tools.
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supplementary info Asterhan, C. S. C., & Resnick, M. (2020).
Refutation texts and argumentation for conceptual change:A winning or a redundant combination?.
Learning & Instruction ,
65.
Publisher's VersionAbstract
Effective instruction for conceptual change should aim to reduce the interference of irrelevant knowledge structures, as well as to improve sense-making of counterintuitive scientific notions. Refutation texts are designed to support such processes, yet evidence for its effect on individual conceptual change of robust, complex misconceptions has not been equivocal. In the present work, we examine whether effects of refutation text reading on conceptual change in biological evolution can be augmented with subsequent peer argumentation activities. Hundred undergraduates read a refutation text followed by either peer argumentation on erroneous worked-out solutions or by standard, individual problem solving. Control group subjects read an expository text followed by individual problem solving. Results showed strong effects for the refutation text. Surprisingly, subsequent peer argumentation did not further improve learning gains after refutation text reading. Dialogue protocols analyses showed that gaining dyads were more likely to be symmetrical and to discuss core conceptual principles.
pdf Ophir, Y., Sisso, I., Asterhan, C. S. C., Tickochinski, R., & Reichart, R. (2020).
The Turker blues: Hidden factors behind increased depression rates in Amazon's Mechanical Turk.
Clinical Psychological Science ,
8 (1), 65-83.
Publisher's VersionAbstractData collection from online platforms, such as Mechanical Turk (MTurk), has become popular in clinical research. However, there are also concerns about the representativeness and the quality of this data for clinical studies. The present work explores these issues in the specific case of major depression. Analyses of two large data sets gathered from MTurk (N1 = 2,692 and N2 = 2,354) revealed two major findings: First, failing to screen for inattentive and fake respondents inflates the rates of major depression artificially and significantly (to 18.5% to 27.5%). Second, after cleaning the data sets, depression in MTurk is still 1.6 to 3.6 times higher than general population estimates. Approximately half of this difference can be attributed to differences in the composition of MTurk samples and the general population (i.e., socio-demographics, health and physical activity lifestyle). Several explanations for the other half are proposed and practical data-quality tools are provided.
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