Researchers aspire to capture various aspects of dialogue emerging in classroom interactions using systematic analyses. Recent years have seen increasing demand for ‘mixed methods’ approaches that offer more information. These approaches build on converging theoretical clarity and understandings, technological tools and better communication among scholars interested in classroom discussions. In this chapter, we showcase several different ways of mixing methods and enriching the information from classroom observations. The first is combining analytic tools to capture and integrate qualitative and quantitative data, and it suggests using network models to interpret the results of dialogue coding. The second suggests examining more than one grain size of analytic unit (e.g., turn, episode and lesson), or coding transcribed interaction in a top-down or bottom-up manner or both. The third approach suggests synthesising across different frameworks of analysis, to understand disciplinary and multimodal dialogues. Sociocultural discourse analysis and epistemic network analysis illustrate multiple approaches.

This study contributes to growing scholarly interest in teacher-led, school-based learning communities and the characteristics of teacher dialogue and social interaction that support professional learning in these settings. Based on existing conceptual distinctions proposed in the literature, we term this type of teacher dialogue "collaborative inquiry into practice" (CLIP) and propose a systematic and reliable tool to measure it. We then employ a quantitative, comparative research design to study how different teacher team activities (i.e., video-analysis, peer consultations, and pedagogical planning) shape the extent to which teachers engage in CLIP. Fifty-four transcribed teacher meeting excerpts were analyzed with the CLIP coding scheme, assessing different aspects of inquiry-based reasoning, participation, and content. Quantitative comparisons and illustrative examples show that CLIP was lowest during peer consultations, in part because teachers were often not positioned as agents of change in such conversations. Pedagogical planning activities featured more instances of inquiry into each other’s ideas. Contrary to common assumptions, collaborative video analysis activities were not characterized by increased attention to student thinking or inquiry orientation. Our findings provide new insights into teacher-led, collaborative learning in on-the-job settings, as well as practical implications for the design of school-based professional learning communities.

Scholarly efforts to identify core design features for effective teacher professional development have grown rapidly in the last 25 years. Many concise lists of design principles have emerged, most of which converge on a consensus of 5-7 presumably "effective” design features (e.g., collaborative tasks, focus on content, active learning). The proliferation and convergence of reviews create the impression that this consensus is based on strong evidence from large-scale, replicated and rigorously controlled research studies. We critique the empirical foundation on which conclusions about evidence-based design features for teacher professional development have been based, by the same evidential standards that have been adopted within this field of scholarly work. We conclude that the empirical foundations for these lists are problematic and that claims to methodological rigor are misleading as they are based on flawed inferences. We further argue that the ambition to identify general features of effective professional development is also problematic, and reflect on why, despite its weaknesses and potentially adverse consequences for research and practice, we as a field continue to herald this consensus. We call for greater focus on the development, testing and refinement of theories about teacher professional learning in order to advance understanding, policy and practice in the field.

Over the past decade, rapid technological advancements and budget constraints have increased the demand for online education (Martin et al., 2020). Furthermore, the COVID-19 pandemic has vastly accelerated this trend, compelling almost all education providers to migrate their courses to online learning platforms (Theelen & Van Breukelen, 2022). In view of other profound crises that affect mobility, such as climate change, political instabilities and future pandemics, it is safe to assume that online learning will remain in demand, even in a post-pandemic world ) (Bayne et al., 2020). In this context, while educational research  has  made  significant  progress  in  establishing  design  principles  that  ensure  effective  online teaching  and  learning,  the  main  focus  of  this  scholarly  work  is  on  the  acquisition  of  declarative knowledge  and  cognitive  skills.  Moreover,  since  very  little  is  known  about  the  online  teaching  and distance  learning  of  psychomotor skills  (Kouhia  et  al.,  2021;  Lehtiniemi  et  al.,  2023),  this  paper  and 
exhibition  explore  how  eye-tracking  technology  (ETT)  creates  unique  opportunities  to  improve  craft education in hybrid and distant learning settings.

Research has shown that student participation in classroom dialogues is associated 
with learning gains, and initiatives to encourage more dialogic forms of learning and teaching 
are abundant. Yet, less is known about how different students may experience, participate in, and what they may gain from dialogic classroom activities. In the current work, we explore potential differences in participation of high and low-achieving students (from 6 different 
classrooms) in upper elementary Hebrew lessons of teachers who participated in a professional development program on academically productive dialogue. We used Epistemic Network Analysis to identify differences across twelve lessons. Findings reveal that the network model of low-achieving students is characterized by simpler talk moves, reduced connectivity, and 
repetitive loops. In contrast, high achieving students’ network model is more interconnected, and the strongest connections formed among codes there are indicative of a reasoned argumentation and critical stance. Analyses of selected excerpts further explored the dynamics that may have led to these different patterns.  

Within the scholarly field of academically productive classroom dialogue, several elusive, yet rich and potent discourse moves have received special attention. However, their rarity and complexity also poses significant challenges to meeting interrater reliability thresholds, and they are often omitted from quantitative research efforts. We propose a different approach for coding that circumvents these issues, called DECCA. In this presentation, we showcase this methodology by focusing on teacher revoice. We demonstrate how it is possible to deconstruct this complex phenomenon into smaller and simpler elements. We then code each turn in the corpus for the existence or absence of each element (which we term Dialogue Elements, DEs). Adding a post-coding, pre-analysis stage allows us to extract turns which contain the specific combination of DEs relevant for revoice and distinguish it from similar teacher dialogue facilitation moves. Theoretical and practical implications are discussed.

During the height of the COVID-19 pandemic, teachers around the globe had beenforced to move their teaching to full-time online, remote teaching. In this study, we aimedat understanding teacher burnout during COVID-19. We conducted a survey among399 teachers at the peak of a prolonged physical school closure. Teachers reportedexperiencing more burnout during (vs. before) the COVID-19 pandemic. Contributingfactors to this burnout were high family work conflict and low online teaching proficiency.Burnout was associated with lower work-related wellbeing: Lower work commitment,and higher turnover intentions. It was also associated with lower psychologicalwellbeing: More depressive and anxiety symptoms, and lower subjective wellbeing.Approach (but not avoid) coping strategies served as a protective factor against theburnout-turnover intentions association. We conclude with recommendations on howto mitigate teacher burnout, thereby contributing to teacher wellbeing.

Academically productive talk (APT) in classrooms has long been associated with significant gains in student learning and development. Yet, due to COVID-19 related restrictions, teachers around the world were forced to adapt their teaching to online, remote settings during the pandemic. In this investigation, we studied APT in junior high school during extended online, remote teaching spells. Specifically, we focused on the extent APT was a part of online teaching practices, what characterized teachers who tended to promote APT more in online, remote teaching, and associations between APT and teacher well-being, as well as student motivation and engagement. Findings from two survey studies (Study 1: 99 teachers, and 83 students; Study 2: 399 teachers) revealed the following patterns: Students and teachers agreed that APT was used to a lesser extent in remote, online classes, and associated with more interactive instructional formats (whole classroom discussion, peer group work, and questioning), but not with frontal teaching and individual task completion. Teachers with a higher sense of teaching self-efficacy, autonomous orientations, and higher empathy tended to promote APT in online, remote teaching more. More APT was associated with greater teachers’ work-related (i.e., lower burnout, more commitment to teaching, and lower turnover intentions) and psychological well-being (i.e., less depressive and anxiety symptoms, and higher subjective well-being). Finally, student experiences with APT in online, remote learning was positively associated with learning motivation and engagement. Theoretical and practical implications are discussed.

We explore how problem framing shapes teacher dialogue in teacher-led, school-based peer consultations. Twenty audio-recorded workgroup conversations were analyzed using a mixed-methods approach. Three different frames for presenting problems of practice were identified: teaching-, student- and classroom composition-oriented. Quantitative analyses showed associations between problem frames and the ensuing positioning of teachers as main agentive actors. In-depth qualitative analysis of two focal cases of low-teacher-agency problem frames (student- and classroom composition-oriented) revealed that psychologized discourses and attribution of responsibility to parents contributed to reduction of teacher responsibility and concomitant limited agency, and that initial problem frames were resistant to reframing.

 Despite visions of social network technology (SNT) for collaborative knowledge construction, recent research in secondary schools suggest that students use these tools mainly for knowledge sharing of study-related artifacts. We extend these findings to higher education settings and report on two survey studies that map characteristics of students’ self-directed use of SNTs for study purposes, in undergraduate university programs (N = 264) and teacher training colleges (N = 449). The combined findings confirm that students use SNTs extensively for uploading, linking and downloading study-related artifacts in peer-directed SNT groups. They regard these practices positively and believe they improve academic achievements. Sharing was predicted by positive attitudes toward sharing and collectivist value orientations, motivated overall by prosocial reasons and less frequent in competitive study programs. Use of shared materials was associated with performance-avoidance achievement goals and lower GPA. Findings, directions for future research and implications are discussed in the context of learning theories, as well the knowledge sharing literature. 

Data 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. 

 Scholarly interest in dialogic pedagogy and classroom dialogue is multi-disciplinary and draws on a variety of theoretical frameworks. On the positive side, this has produced a rich and varied body of research and evidence. However, in spite of a common interest in educational dialogue and learning through dialogue, cross-disciplinary engagement with each other’s work is rare. Scholarly discussions and publications tend to be clustered in separate communities, each characterized by a particular type of research questions, aspects of dialogue they focus on, type of evidence they bring to bear, and ways in which standards for rigor are constructed. In the present contribution, we asked four leading scholars from different research traditions to react to four provocative statements that were deliberately designed to reveal areas of consensus and disagreement. Topic-wise, the provocations related to theoretical foundations, methodological assumptions, the role of teachers, and issues of inclusion and social class, respectively. We hope that these contributions will stimulate cross- and trans-disciplinary discussions about dialogic pedagogy research and theory.    

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 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. 

Scholarly 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.

This 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. 

 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. 

 Online Social Networking Sites (SNSs) are immensely popular, especially among adolescents. Activity on these sites leaves digital footprints, which may be used to study online behavioral correlates of adolescent psychological distress and to, ultimately, improve detection and intervention efforts. In the present work, we explore the digital footprints of adolescent depression, social rejection, and victimization of bullying on Facebook. Two consecutive studies were conducted among Israeli adolescents (N = 86 and N = 162). We collected a range of Facebook activity features, as well as self-report measurements of depression, social rejection, and victimization of bullying. Findings from Study 1 demonstrate that explicit distress references in Facebook postings (e.g., "Life sucks, I want to die") predict depression among adolescents, but that such explicit distress references are rare. In Study 2, we applied a bottom-up research methodology along with the previous top-down, theory driven approach. Study 2 demonstrates that less explicit features of Facebook behavior predict social rejection and victimization of bullying. These features include ’posts by others’, ’check-ins’, ’gothic and dark content’, ’other people in pictures’, and ’positive attitudes towards others’. The potential, promises and limitations of using digital Facebook footprints for the detection of adolescent psychological distress are discussed.