High staff turnover in higher education often burdens teachers with laborious handovers of teaching tasks every semester. To boost teachers' workflow efficiency, we present an innovative knowledge management tool that allows experienced teachers to seamlessly capture task steps (i.e., denoted as ShareFlow) that can be subsequently recommended to novices via proactive push, all happening during teachers' natural workflow to minimize disruptions. We conducted a controlled experiment with 30 participants and compared our tool against a state-of-the-art baseline knowledge management system powered by a large language model (Claude 3 Haiku). We found that our knowledge management tool reduced task completion time and improved task quality (with statistical significance). Feedback from the participants also indicated the high usability of our tool, suggesting its strong potential for practical adoption for improving teacher workflows.

Knowledge management involves finding, expanding, and using knowledge in an organisation to achieve goals. Its role is crucial in higher education to improve problem-solving, research, and teaching by acquiring, sharing, and applying knowledge. Higher education institutions can promote knowledge management through Communities of Practice, but doing so remains challenging due to cultural, organisational, and technological reasons. We present findings of the first step of co-design workshops with authentic higher education teaching teams that sought to understand (a) their practices as a community and any motivators and impediments to their community development; (b) how they perceived the tools they use for knowledge management; and (c) the kinds of tools they believed could help them better conduct knowledge management and develop as Communities of Practice. Our findings suggested four essential design requirements and informed our development of a new tool to support the knowledge management needs of higher education teaching teams.

The recent large language models (LLMs), e.g., ChatGPT, have been able to generate human-like and fluent responses when provided with specific instructions. While admitting the convenience brought by technological advancement, educators also have concerns that students might leverage LLMs to complete their writing assignments and pass them off as their original work. Although many AI content detection studies have been conducted as a result of such concerns, most of these prior studies modeled AI content detection as a classification problem, assuming that a text is either entirely human-written or entirely AI-generated. In this study, we investigated AI content detection in a rarely explored yet realistic setting where the text to be detected is collaboratively written by human and generative LLMs (termed as hybrid text for simplicity). We first formalized the detection task as identifying the transition points between human-written content and AI-generated content from a given hybrid text (boundary detection). We constructed a hybrid essay dataset by partially and randomly removing sentences from the original student-written essays and then instructing ChatGPT to fill in for the incomplete essays. Then we proposed a two-step detection approach where we (1) separated AI-generated content from human-written content during the encoder training process; and (2) calculated the distances between every two adjacent prototypes (a prototype is the mean of a set of consecutive sentences from the hybrid text in the embedding space) and assumed that the boundaries exist between the two adjacent prototypes that have the furthest distance from each other. Through extensive experiments, we observed the following main findings: (1) the proposed approach consistently outperformed the baseline methods across different experiment settings; (2) the encoder training process (i.e., step 1 of the above two-step approach) can significantly boost the performance of the proposed approach; (3) when detecting boundaries for single-boundary hybrid essays, the proposed approach could be enhanced by adopting a relatively large prototype size (i.e., the number of sentences needed to calculate a prototype), leading to a 22% improvement (against the best baseline method) in the In-Domain evaluation and an 18% improvement in the Out-of-Domain evaluation.

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.

The past few years have witnessed an explosion of attention given to the bias displayed by Machine Learning (ML) techniques towards different groups of people (e.g., female vs. male). Although ML techniques have been widely adopted in education, it remains largely unexplored that to what extent such ML bias manifests itself in this specific setting and how it can be reduced and eliminated. Given the increasing importance of ML techniques in empowering educators to teach effectively, this study aimed to quantify the characteristics of the original datasets that might be correlated with the subsequent predictive unfairness displayed by ML models. To this end, we empirically investigated two types of data biases (i.e., distribution bias and hardness bias) towards students of different sexes and first-language backgrounds across a total of five frequently-performed predictive tasks in education. Then, to improve ML fairness, we drew inspiration from the well-established research in Class Balancing Techniques (CBTs), where samples are generated/removed to alleviate the predictive disparity between different prediction classes. We proposed two simple but effective strategies to empower class balancing techniques for alleviating data biases and improving prediction fairness. Through extensive analyses and evaluations, we demonstrated that ML models may greatly improve prediction fairness (improvement up to 66%) with only a small sacrifice (less than 1%) in prediction accuracy by balancing the training data with the use of students’ demographic information and the overall hardness bias measure. All data and code used in this study are publicly accessible via https://github.com/lsha49/FairEdu.

Pretrained Language Models (PLMs), though popular, have been diagnosed to encode bias against protected groups in the representations they learn, which may harm the prediction fairness of downstream models. Given that such bias is believed to be related to the amount of demographic information carried in the learned representations, this study aimed to quantify the awareness that a PLM (i.e., BERT) has regarding people’s protected attributes and augment BERT to improve prediction fairness of downstream models by inhibiting this awareness. Specifically, we developed a method to dynamically sample data to continue the pretraining of BERT and enable it to generate representations carrying minimal demographic information, which can be directly used as input to downstream models for fairer predictions. By experimenting on the task of classifying educational forum posts and measuring fairness between students of different gender or first-language backgrounds, we showed that, compared to a baseline without any additional pretraining, our method improved not only fairness (with a maximum improvement of 52.33%) but also accuracy (with a maximum improvement of 2.53%). Our method can be generalized to any PLM and demographic attributes. All the codes used in this study can be accessed via https://github.com/lsha49/FairBERT_deploy.