Predictive modeling is a core technique used in tackling various tasks in learning analytics research, e.g., classifying educational forum posts, predicting learning performance, and identifying at-risk students. When applying a predictive model, it is often treated as the first priority to improve its prediction accuracy as much as possible. Class balancing, which aims to adjust the unbalanced data samples of different class labels before using them as input to train a predictive model, has been widely regarded as a powerful method for boosting prediction accuracy. However, its impact on algorithmic bias remains largely unexplored, i.e., whether the use of class balancing methods would alleviate or amplify the differentiated prediction accuracy received by different groups of students (e.g., female versus male). To fill this gap, our study selected three representative predictive tasks as the testbed, based on which we 1) applied two well known metrics (i.e., hardness bias and distribution bias) to measure data characteristics to which algorithmic bias might be attributed; and 2) investigated the impact of a total of eleven class balancing techniques on prediction fairness. Through extensive analysis and evaluation, we found that class balancing techniques, in general, tended to improve predictive fairness between different groups of students. Furthermore, class balancing techniques (e.g., SMOTE and ADASYN), which add samples to the minority group (i.e., oversampling) can enhance the predictive accuracy of the minority group while not negatively affecting the majority group. Consequently, both fairness and accuracy can be improved by applying these oversampling class balancing methods. All data and code used in this study are publicly accessible via https://github.com/lsha49/FairCBT .

Effective collaboration and teamwork skills are critical in high‐risk sectors, as deficiencies in these areas can result in injuries and risk of death. To foster the growth of these vital skills, immersive learning spaces have been created to simulate real‐world scenarios, enabling students to safely improve their teamwork abilities. In such learning environments, multiple dialogue segments can occur concurrently as students independently organise themselves to tackle tasks in parallel across diverse spatial locations. This complex situation creates challenges for educators in assessing teamwork and for students in reflecting on their performance, especially considering the importance of effective communication in embodied teamwork. To address this, we propose an automated approach for generating teamwork analytics based on spatial and speech data. We illustrate this approach within a dynamic, immersive healthcare learning environment centred on embodied teamwork. Moreover, we evaluated whether the automated approach can produce transcriptions and epistemic networks of spatially distributed dialogue segments with a quality comparable to those generated manually for research objectives. This paper makes two key contributions: (1) it proposes an approach that integrates automated speech recognition and natural language processing techniques to automate the transcription and coding of team communication and generate analytics; and (2) it provides analyses of the errors in outputs generated by those techniques, offering insights for researchers and practitioners involved in the design of similar systems. Practitioner notes: What is currently known about this topic Immersive learning environments simulate real‐world situations, helping students improve their teamwork skills. In these settings, students can have multiple simultaneous conversations while working together on tasks at different physical locations. The dynamic nature of these interactions makes it hard for teachers to assess teamwork and communication and for students to reflect on their performance. What this paper adds We propose a method that employs multimodal learning analytics for automatically generating teamwork‐related insights into the content of student conversations. This data processing method allows for automatically transcribing and coding spatially distributed dialogue segments generated from students working in teams in an immersive learning environment and enables downstream analysis. This approach uses spatial analytics, natural language processing and automated speech recognition techniques. Implications for practitioners Automated coding of dialogue segments among team members can help create analytical tools to assist in evaluating and reflecting on teamwork. By analysing spatial and speech data, it is possible to apply learning analytics advancements to support teaching and learning in fast‐paced physical learning spaces where students can freely engage with one another.

Machine Learning (ML) techniques have been increasingly adopted to support various activities in education, including being applied in important contexts such as college admission and scholarship allocation. In addition to being accurate, the application of these techniques has to be fair, i.e., displaying no discrimination towards any group of stakeholders in education (mainly students and instructors) based on their protective attributes (e.g., gender and age). The past few years have witnessed an explosion of attention given to the predictive bias of ML techniques in education. Though certain endeavors have been made to detect and alleviate predictive bias in learning analytics, it is still hard for newcomers to penetrate. To address this, we systematically reviewed existing studies on predictive bias in education, and a total of 49 peer-reviewed empirical papers published after 2010 were included in this study. In particular, these papers were reviewed and summarized from the following three perspectives: (i) protective attributes, (ii) fairness measures and their applications in various educational tasks, and (iii) strategies for enhancing predictive fairness. These findings were summarized into recommendations to guide future endeavors in this strand of research, e.g., collecting and sharing more quality data containing protective attributes, developing fairness-enhancing approaches which do not require the explicit use of protective attributes, validating the effectiveness of fairness-enhancing on students and instructors in real-world settings.

In online courses, discussion forums play a key role in enhancing student interaction with peers and instructors. Due to large enrolment sizes, instructors often struggle to respond to students in a timely manner. To address this problem, both traditional Machine Learning (ML) (e.g., Random Forest) and Deep Learning (DL) approaches have been applied to classify educational forum posts (e.g., those required urgent responses vs. that did not). However, there lacks an in-depth comparison between these two kinds of approaches. To better guide people to select an appropriate model, we aimed at providing a comparative study on the effectiveness of six frequently-used traditional ML and DL models across a total of seven different classification tasks centering around two datasets of educational forum posts. Through extensive evaluation, we showed that (i) the up-to-date DL approaches did not necessarily outperform traditional ML approaches; (ii) the performance gap between the two kinds of approaches can be up to 3.68% (measured in F1 score); (iii) the traditional ML approaches should be equipped with carefully-designed features, especially those of common importance across different classification tasks. Based on the derived findings, we further provided insights to help instructors and educators construct effective classifiers for characterizing educational forum discussions, which, ultimately, would enable them to provide students with timely and personalized learning support.