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This descriptive study focuses on using voice activity detection (VAD) algorithms to extract student speech data in order to better understand the collaboration of small group work and the impact of teaching assistant (TA) interventions in undergraduate engineering discussion sections. Audio data were recorded from individual students wearing head‐mounted noise‐cancelling microphones. Video data of each student group were manually coded for collaborative behaviours (eg, group task relatedness, group verbal interaction and group talk content) of students and TA–student interactions. The analysis includes information about the turn taking, overall speech duration patterns and amounts of overlapping speech observed both when TAs were intervening with groups and when they were not. We found that TAs very rarely provided explicit support regarding collaboration. Key speech metrics, such as amount of turn overlap and maximum turn duration, revealed important information about the nature of student small group discussions and TA interventions. TA interactions during small group collaboration are complex and require nuanced treatments when considering the design of supportive tools. Practitioner notesWhat is already known about this topic Student turn taking can provide information about the nature of student discussions and collaboration.Real classroom audio data of small groups typically have lots of background noise and present challenges for audio analysis.TAs have little training in how to productively intervene with students about collaborative skills.What this paper adds TA interaction with groups primarily focused on task progress and understanding of concepts with negligible explicit support on building collaborative skills.TAs intervened with the groups often which gave the students little time for uptake of their suggestions or deeper discussion.Student turn overlap was higher without the presence of TAs.Maximum turn duration can be an important real‐time turn metric to identify the least verbally active student participant in a group.Implications for practice and/or policy TA training should include information about how to monitor groups for collaborative behaviours and when and how they should intervene to provide feedback and support.TA feedback systems should keep track of previous interventions by TAs (especially in contexts where there are multiple TAs facilitating) and the duration since previous intervention to ensure that TAs do not intervene with a group too frequently with little time for student uptake.Maximum turn duration could be used as a real‐time metric to identify the least verbally active student in a group so that support could be provided to them by the TAs. [ABSTRACT FROM AUTHOR]

The objective of this study is to explore new ways of assessing collaborative problem solving (CPS) processes based on different modalities of audio data and their combination. The data collection took place in an educational lab setting during an experiment with adult teams from professional contexts who collaboratively solved multiple problems as part of a CPS training. From audio data, both verbal (ie, speech) and non‐verbal (ie, pitch) aspects were extracted. Four analysis methods were used, including (a) content analysis; (b) linguistic inquiry and word count; (c) verbal entrainment analysis; and (d) acoustic–prosodic entrainment based on pitch data. Insights are given into the CPS processes of the participating groups using these measures and relevant relationships between some of these measures are further investigated. Based on content analysis, it was found that most of the interactions during the CPS process are task oriented, whereas team‐oriented interactions are less present. Second, three measures of proportion of contribution in CPS were investigated and clear differences in participation patterns between and within teams were found. We suggest that a combination of utterance count and words per sentence could provide valuable insights for quantity and equality of participation. Third, the study explored pronoun use and found that the most frequently used personal pronouns were first‐person singular. Next, the results indicated a relationship between pronoun use and the relative frequency of interactions. Fourth, a rather weak relationship between lexical entrainment measures and the acoustic–prosodic measures were found, suggesting that these measures are indicative of separate communicative aspects in CPS. This study contributes to a better understanding of which type of audio‐based data is most informative to teachers and students as a feedback or assessment tool. This study complements previous research as it focuses on spoken human‐to‐human communication collected in an authentic context. Practitioner notesWhat is already known about this topic Support and guidance systems for learning coaches, teachers and learners are needed to foster the educational quality of collaborative problem solving (CPS) activities.CPS is a complex process and measuring the quality of CPS processes remains challenging.Multimodal learning analytics, focusing on verbal and non‐verbal data sources and using content analysis, linguistic inquiry and word count and verbal and acoustic entrainment measures could be valuable to measure the quality of CPS.What this paper adds The majority of interactions during CPS processes are task oriented or cognitive of nature, whereas team‐oriented interactions are less present.Utterance count and words per sentence should be used in combination, as they are indicative of different aspects.Pronoun use in learners' discourse is related to the types of CPS interactions.Lexical entrainment measures and acoustic–prosodic are indicative of distinctive communicative aspects in CPS.Implications for practice and/or policy Quality indicators of CPS processes should include both verbal and non‐verbal measures of students' interactions.Educational researchers and the (Edtech) industry should further leverage their forces to foster the development of (semi‐)automated systems for measuring the quality of CPS processes.It should be further investigated how quality indicators of CPS processes can be most meaningful to trainers, teachers and learners, for example, through the use of dashboards. [ABSTRACT FROM AUTHOR]

Computational thinking (CT), which is a cognitive skill used to solve problems with computational solutions, has drawn increasing attention among researchers and practitioners due to the growing recognition of CT competence as a 21st century skill. Collaboration is commonly integrated into CT education to facilitate novice learning, but there is inadequate knowledge regarding the influences of collaboration in CT education. This meta‐analysis examined the overall effects on the cognitive, social and affective competencies of collaborative versus individual problem solving in CT through programming. We identified 33 publications involving 4717 learners, which allowed for 220 effect size comparisons. We found a medium effect size (Hedges' g = 0.562; p < 0.001) in favour of collaborative problem solving on cognitive learning outcomes and a small effect size (Hedges' g = 0.316; p < 0.01) on affective learning outcomes using a random‐effects model. Categorical moderator analysis revealed the moderating effects of educational level, programming environment, study duration, grouping method and group size. The competency model that was generated from the synthesized literature on collaborative computational problem solving yielded insights into the learning design and assessment of relevant activities. Practitioner notesWhat is already known about the topic?Although professional programming usually requires collaboration to accomplish common goals in the work environment, programming is predominantly taught on an individual basis in educational settings.Some intervention studies have reported inconsistent learning outcomes (positive and negative) from integrating collaborative problem solving (CPS) into programming education.There is a need for further scrutiny and exploration of the role of collaboration in the process of learning to program.What does this paper add?Our meta‐analysis showed that CPS is an effective pedagogical tool for developing students' cognitive, affective and social competencies.The moderator analysis revealed that CPS in programming has cognitive benefits irrespective of grouping method, group size and educational levels; however, there is a need for caution in regard to designing collaborative problem‐solving activities for different programming environments and durations.The model generated from the literature synthesis can yield insights into better assessing and designing collaborative problem‐solving activities.Implications for practice and/or policy?Integrating CPS into programming education can prepare students with 21st century skills.Social and affective competencies are crucial to students' learning capacity, which is an issue that needs to be better assessed.Future studies could look more rigorously into the effects of CPS in visual programming and robotics programming among younger age groups, such as primary or middle school students. [ABSTRACT FROM AUTHOR]