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Social interactions among classroom peers, represented as social learning networks (SLNs), play a crucial role in enhancing learning outcomes. While SLN analysis has recently garnered attention, most existing approaches rely on centralized training, where data is aggregated and processed on a local/cloud server with direct access to raw data. However, in real-world educational settings, such direct access across multiple classrooms is often restricted due to privacy concerns. Furthermore, training models on isolated classroom data prevents the identification of common interaction patterns that exist across multiple classrooms, thereby limiting model performance. To address these challenges, we propose one of the first frameworks that integrates Federated Learning (FL), a distributed and collaborative machine learning (ML) paradigm, with SLNs derived from students' interactions in multiple classrooms' online forums to predict future link formations (i.e., interactions) among students. By leveraging FL, our approach enables collaborative model training across multiple classrooms while preserving data privacy, as it eliminates the need for raw data centralization. Recognizing that each classroom may exhibit unique student interaction dynamics, we further employ model personalization techniques to adapt the FL model to individual classroom characteristics. Our results demonstrate the effectiveness of our approach in capturing both shared and classroom-specific representations of student interactions in SLNs. Additionally, we utilize explainable AI (XAI) techniques to interpret model predictions, identifying key factors that influence link formation across different classrooms. These insights unveil the drivers of social learning interactions within a privacy-preserving, collaborative, and distributed ML framework—an aspect that has not been explored before. 

Question-asking is a crucial learning and teaching approach. It reveals different levels of students' understanding, application, and potential misconceptions. Previous studies have categorized question types into higher and lower orders, finding positive and significant associations between higher-order questions and students' critical thinking ability and their learning outcomes in different learning contexts. However, the diversity of higher-order questions, especially in collaborative learning environments. has left open the question of how they may be different from other types of dialogue that emerge from students' conversations, To address these questions, our study utilized natural language processing techniques to build a model and investigate the characteristics of students' higher-order questions. We interpreted these questions using Bloom's taxonomy, and our results reveal three types of higher-order questions during collaborative problem-solving. Students often use Why, How and What If' questions to I) understand the reason and thought process behind their partners' actions: 2) explore and analyze the project by pinpointing the problem: and 3) propose and evaluate ideas or alternative solutions. In addition. we found dialogue labeled 'Social'. 'Question - other', 'Directed at Agent', and 'Confusion/Help Seeking' shows similar underlying patterns to higher-order questions, Our findings provide insight into the different scenarios driving students' higher-order questions and inform the design of adaptive systems to deliver personalized feedback based on students' questions. 

Promptly addressing students’ help requests on their programming assignments has become more and more challenging in computer science education. Since the pandemic, most instructors use online office hours to answer questions. Prior studies have shown increased student participation with online office hours. This popularity has led to significantly longer wait times in the office hours queue, and various strategies for selecting the next student to help may impact wait time. For example, prioritizing students who have not been seen on the day of the deadline will extend the wait time for students who are frequently rejoining the queue. To better understand this problem, we explored students’ behavior when they are waiting in the queue. We investigate the amount of time students are willing to wait in the queue by modeling the distribution of cancellation time. We find that after waiting for 49 minutes, most students will cancel their help request. Then, we looked at students’ coding actions during the waiting period and found that only 21% of students have commits while waiting. Surprisingly, students who waited for hours did not commit their work for automated feedback. Our findings suggest that time in the queue should be considered in addition to other factors like last interaction when selecting the next student to help during office hours to minimize canceled interactions. 

Intelligent systems to support collaborative learning rely on real-time behavioral data, including language, audio, and video. However, noisy data, such as word errors in speech recognition, audio static or background noise, and facial mistracking in video, often limit the utility of multimodal data. It is an open question of how we can build reliable multimodal models in the face of substantial data noise. In this paper, we investigate the impact of data noise on the recognition of confusion and conflict moments during collaborative programming sessions by 25 dyads of elementary school learners. We measure language errors with word error rate (WER), audio noise with speech-to-noise ratio (SNR), and video errors with frame-by-frame facial tracking accuracy. The results showed that the model’s accuracy for detecting confusion and conflict in the language modality decreased drastically from 0.84 to 0.73 when the WER exceeded 20%. Similarly, in the audio modality, the model’s accuracy decreased sharply from 0.79 to 0.61 when the SNR dropped below 5 dB. Conversely, the model’s accuracy remained relatively constant in the video modality at a comparable level (> 0.70) so long as at least one learner’s face was successfully tracked. Moreover, we trained several multimodal models and found that integrating multimodal data could effectively offset the negative effect of noise in unimodal data, ultimately leading to improved accuracy in recognizing confusion and conflict. These findings have practical implications for the future deployment of intelligent systems that support collaborative learning in actual classroom settings. 

In Computer Science (CS) education, instructors use office hours for one-on-one help-seeking. Prior work has shown that traditional in-person office hours may be underutilized. In response many instructors are adding or transitioning to virtual office hours. Our research focuses on comparing in-person and online office hours to investigate differences between performance, interaction time, and the characteristics of the students who utilize in-person and virtual office hours. We analyze a rich dataset covering two semesters of a CS2 course which used in-person office hours in Fall 2019 and virtual office hours in Fall 2020. Our data covers students' use of office hours, the nature of their questions, and the time spent receiving help as well as demographic and attitude data. Our results show no relationship between student's attendance in office hours and class performance. However we found that female students attended office hours more frequently, as did students with a fixed mindset in computing, and those with weaker skills in transferring theory to practice. We also found that students with low confidence in or low enjoyment toward CS were more active in virtual office hours. Finally, we observed a significant correlation between students attending virtual office hours and an increased interest in CS study; while students attending in-person office hours tend to show an increase in their growth mindset. 

In Computer Science (CS) education, instructors use office hours for one-on-one help-seeking. Prior work has shown that traditional in-person office hours may be underutilized. In response many instructors are adding or transitioning to virtual office hours. Our research focuses on comparing in-person and online office hours to investigate differences between performance, interaction time, and the characteristics of the students who utilize in-person and virtual office hours. We analyze a rich dataset covering two semesters of a CS2 course which used in-person office hours in Fall 2019 and virtual office hours in Fall 2020. Our data covers students' use of office hours, the nature of their questions, and the time spent receiving help as well as demographic and attitude data. Our results show no relationship between student's attendance in office hours and class performance. However we found that female students attended office hours more frequently, as did students with a fixed mindset in computing, and those with weaker skills in transferring theory to practice. We also found that students with low confidence in or low enjoyment toward CS were more active in virtual office hours. Finally, we observed a significant correlation between students attending virtual office hours and an increased interest in CS study; while students attending in-person office hours tend to show an increase in their growth mindset.