Modern courses require students to use multiple digital learning platforms, but traditional learning analytics often focuses on single, specific platforms. Thus, there is a limited understanding of how students integrate information across sources. We present an alternative approach using a browser extension, which collects behavior information across multiple web-based learning platforms simultaneously. We tested the extension in a college-level introductory statistics course with 27 students over 15 weeks. Using the information collected by the extension, we found that students navigate multi-platform learning environments in diverse ways, which can inform curriculum design and the effectiveness of learning platforms.

Students struggle with accurately assessing their own performance, especially given little training to do so.We propose an AI-powered training tool to help students improve "metacognitive calibration, " or the ability to accurately predict their own learning, potentially enhancing learning outcomes by enabling students' use of metacognitioninformed learning behaviors.We present results from a randomized controlled trial (N = 133) assessing the effectiveness of the tool in a college-level computer-based learning environment.The AIdriven tool significantly improved learning gains compared to the control group by 8.9% (t = -2.384,p = .019),and this effect was significantly mediated by learning behaviors.Overconfident students who received the intervention showed significantly greater metacognitive calibration improvement than the control group by 4.1% (t = 2.001, p = .049).These insights highlight the value of AIpowered metacognitive calibration training and the importance of promoting specific metacognition-informed learning behaviors in computer-based learning.

This paper studies a bidirectional human-AI collaborative student performance prediction problem to enhance equitable online education, aligning with the United Nations' Sustainable Development Goal (SDG) of ensuring inclusive and equitable quality education for all. The goal is to leverage collaborative intelligence to generate accurate and fair student outcome predictions from behavioral data, ensuring equitable estimation for underrepresented populations. Current fair AI solutions often fail to mitigate demographic bias in the absence of student demographic data, while human-AI collaborative approaches frequently overlook human cognitive biases, leading to inaccurate predictions. We develop CollabDebias, a novel bidirectional human-AI collaborative framework that utilizes the complementary strengths of AI and humans to mitigate the AI demographic bias and human cognitive bias. To address AI demographic bias, we propose an uncertainty learning-based bias identification method and a reliability-aware human-AI integration approach. To reduce human cognitive bias, we design uncertainty-aware visualization of AI decision area and attention mechanism. Experimental results on an online course demonstrate CollabDebias's effectiveness in improving student performance prediction accuracy and fairness.