Jianjun Xiao

Jianjun Xiao, Cixiao Wang, Wenmei Zhang

Measuring collaborative problem solving (CPS) synergy remains challenging in learning analytics, as classical manual coding cannot capture emergent system-level dynamics. This study introduces a computational framework that integrates automated discourse analysis with the Synergy Degree Model (SDM) to quantify CPS synergy from group communication. Data were collected from 52 learners in 12 groups during a 5-week connectivist MOOC (cMOOC) activity. Nine classification models were applied to automatically identify ten CPS behaviors across four interaction levels: operation, wayfinding, sense-making, and creation. While BERT achieved the highest accuracy, GPT models demonstrated superior precision suitable for human-AI collaborative coding. Within the SDM framework, each interaction level was treated as a subsystem to compute group-level order parameters and derive synergy degrees. Permutation tests showed automated measures preserve construct validity, despite systematic biases at the subsystem level. Statistical analyses revealed significant task-type differences: survey study groups exhibited higher creation-order than mode study groups, suggesting "controlled disorder" may benefit complex problem solving. Importantly, synergy degree distinguished collaborative quality, ranging from excellent to failing groups. Findings establish synergy degree as a sensitive indicator of collaboration and demonstrate the feasibility of scaling fine-grained CPS analytics through AI-in-the-loop approaches.

Jianjun Xiao, Cixiao Wang

Connectivist MOOCs (cMOOCs) rely on learner-driven interaction, yet their intentionally light facilitation makes it difficult to design generative AI participation that is both scalable and educationally productive. This design-based research study examined how human-AI interaction can be designed for discussion facilitation through a collaborative AI-in-the-loop workflow. Across two iterations in a five-week cMOOC (N = 606), we designed, deployed, and evaluated a facilitation system that combined network-structure-driven target selection, discourse-adaptive response roles, and mandatory human review before AI participation became visible in the community. Iteration 1 (Weeks 1-2) focused on refining the interaction design, showing that the most sustainable facilitation patterns were Guide (70.4%) and Amplifier (28.5%) responses and yielding explicit moderation standards for publishable AI participation. Iteration 2 (Weeks 3-5) examined how different forms of AI-mediated interaction related to social and cognitive presence. AI participation selectively enhanced Open Communication (r = 0.188, p = 0.006), Networked Cohesion (r = 0.274, p < 0.001), and overall social presence (r = 0.162, p = 0.015), while cognitive presence showed no overall improvement. More importantly, direct learner-agent interaction was associated with significantly higher social presence (r = 0.186, p = 0.004) and higher-order cognitive indicators-Integration (r = 0.206, p = 0.001) and Resolution/Creation (r = 0.350, p < 0.001)-than mere co-presence in AI-involved threads. The findings suggest that effective GenAI-supported discussion depends less on AI presence alone than on interaction design: reciprocal exchange, discourse-adaptive facilitation roles, and collaborative human review appear to be key conditions for productive AI participation in online learning communities.

Jianjun Xiao, Yuxi Long

Internet technologies have expanded higher education students' access to learning resources, peer guidance, and skill-development opportunities beyond formal curricula. Yet the ways students assemble these distributed online resources into coherent learning pathways remain insufficiently understood. This study examines how STEM students construct digital informal learning portfolios through internet-mediated peer advice and platform use. Drawing on Social Cognitive Career Theory (SCCT) and informal learning frameworks, we analyze 3,607 peer advice posts from a large online student community using Computational Grounded Theory (CGT). Results show that career pathway (69.6% of coded documents) and career orientation (59.7%) are the dominant organizing dimensions, yielding three distinct digital informal learning portfolios: a graduate-study portfolio centered on competition training, mathematical foundations, and staged preparation; an industry-employment portfolio centered on self-directed skill building, online platform learning, and strategically timed internships; and a public-sector portfolio characterized by dual-track hedging across graduate study, enterprise employment, and public-sector preparation pathways. The online peer community itself functions as a distributed informal curriculum, collectively producing and transmitting pathway-specific guidance about what to learn, when to learn it, and which internet resources to prioritize. These findings extend SCCT into the domain of internet-mediated digital informal learning and introduce career front-loading as a pattern of early learning reorganization. Implications are discussed for institutional learning support, recognition of internet-enabled learning, and the design of digital guidance infrastructures in higher education.