Abstract. Supporting learners during Collaborative Problem Solving (CPS) is a necessity. Existing studies have compared scaffolds with maximal and minimal instructional support by studying their effects on learning and behaviour. However, our understanding of how such scaffolds could differently shape the distribution of individual engagement and behaviours across different CPS phases remains limited. This study applied Heterogeneous Interaction Network Analysis (HINA) and Sequential Pattern Mining (SPM) to uncover the structural effects of scaffolding on different phases of the CPS process among 78 students aged 14 - 15 years in authentic educational settings. Students with the maximal scaffold demonstrated higher dialogic engagement across more phases than those with the minimal scaffold. However, they demonstrated extensive scripting behaviours across the phases, evidencing the presence of overscripting. Although students with the minimal scaffold demonstrated more problem solving behaviours and fewer scripting behaviours across the phases, they repeated particular behaviours in multiple phases and progressed more to socialising behaviours. In both scaffold conditions, problem solving behaviours rarely progressed to other problem solving behaviours. The paper discusses the implications for scaffold design and teaching practice of CPS, and highlights the distinct yet complementary value of HINA and SPM approaches to investigate students’ learning processes during CPS.

Individual behavioral engagement is an important indicator of active learning in collaborative settings, encompassing multidimensional behaviors mediated through various interaction modes. Little existing work has explored the use of multimodal process data to understand individual behavioral engagement in face-to-face collaborative learning settings. In this study we bridge this gap, for the first time, introducing a heterogeneous tripartite network approach to analyze the interconnections among multimodal process data in collaborative learning. Students’ behavioral engagement strategies are analyzed based on their interaction patterns with various spatial locations and verbal communication types using a heterogeneous tripartite network. The multimodal collaborative learning process data were collected from 15 teams of four students. We conducted stochastic blockmodeling on a projection of the heterogeneous tripartite network to cluster students into groups that shared similar spatial and oral engagement patterns. We found two distinct clusters of students, whose characteristic behavioural engagement strategies were identified by extracting interaction patterns that were statistically significant relative to a multinomial null model. The two identified clusters also exhibited a statistically significant difference regarding students’ perceived collaboration satisfaction and teacher-assessed team performance level. This study advances collaboration analytics methodology and provides new insights into personalized support in collaborative learning.

Generative artificial intelligence (AI) tools utilize machine learning models to create new content in response to human-provided prompts, which can automate the creation of large amounts of content in a short time. This study analyzed students' information behavior in small group discussions where the students were encouraged to use generative AI tools. Descriptive and lag sequential analysis methods were employed to examine the characteristics and patterns of students' information behavior according to Ellis' model of information seeking.The results indicated that although students frequently used generative AI tools as primary information sources, they also sought additional resources to satisfy their informational needs. Additionally, students sometimes copied and pasted useful information from generative AI tools into group documents to share with their group members. Lag sequential analysis revealed that students typically began their information seeking process with generative AI tools, followed by exploring additional information sources.

This work-in-progress paper employs a machine learning method for the automated analysis of research interests in Artificial Intelligence in Education (AIED) at scale. We aim to analyze the essential techniques and critical educational problems studied by researchers in five AIED-related conferences and journals between 2010 and 2022. We trained and compared different machine learning models and feature extraction techniques to achieve the research objective. After comparing different models and hyperparameter combinations, our classifier achieves an accuracy of 0.87 and Cohen's kappa of 0.80. Based on the classification results, we identified the top 10 most frequent keywords within each category for every four year period over the past 12 years. Using the classifier, the 10,723 keywords from 2,684 articles were classified into three categories: educational foci, technical solutions, and AIED applications. We find that 'natural language processing' and 'machine learning' are the primary technical keywords in AIED research, and 'deep learning' and 'artificial intelligence' are the trending technical keywords since 2017. Meanwhile, 'massive open online courses', 'self-regulated learning', 'feedback', 'collaborative learning', and 'online learning' are the top educational foci in the field over the last 12 years. 'Intelligent tutoring systems', 'educational data mining', 'knowledge tracing', and 'learning analytics' continue to receive attention as AIED applications of sustained interest. This study helps to understand the educational foci and technical solutions of AIED research at scale and provides insights into the future of AIED research.

In this study, we explored the relationships between the categories of social presence and teaching presence within theory-oriented and application-oriented sessions of an online course on databases in Bilibili, a popular Chinese video-based social media platform. Teaching presence in video-based learning was analyzed based on the video content, and students' social presence was analyzed based on 3,456 'Danmaku', the synchronized on-screen comments posted by users while watching the teaching video in each episode. Significant differences were observed in the frequencies of co-occurrence among pairs of teaching and social presence categories when comparing theory- and application-oriented sessions, as confirmed through chi-square testing. This study contributes to the theoretical development of understanding the interactions between teaching presence and social presence in video-based online learning, and provides insights for the design of effective video-based online learning resources.