While the capacity to self-regulate has been found to be crucial for secondary school students, prior studies often rely on self-report surveys and think-aloud protocols that present notable limitations in capturing self-regulated learning (SRL) processes. This study advances the understanding of SRL in secondary education by using trace data to examine SRL processes during multi-source writing tasks, with higher education participants included for comparison. We collected fine-grained trace data from 66 secondary school students and 59 university students working on the same writing tasks within a shared SRL-oriented learning environment. The data were labelled using Bannert's validated SRL coding scheme to reflect specific SRL processes, and we examined the relationship between these processes, essay performance, and educational levels. Using epistemic network analysis (ENA) to model and visualise the interconnected SRL processes in Bannert's coding scheme, we found that: (a) secondary school students predominantly engaged in three SRL processes - Orientation, Re-reading, and Elaboration/Organisation; (b) high-performing secondary students engaged more in Re-reading, while low-performing students showed more Orientation process; and (c) higher education students exhibited more diverse SRL processes such as Monitoring and Evaluation than their secondary education counterparts, who heavily relied on following task instructions and rubrics to guide their writing. These findings highlight the necessity of designing scaffolding tools and developing teacher training programs to enhance awareness and development of SRL skills for secondary school learners.

In computer-based learning environments (CBLEs), adopting effective self-regulated learning (SRL) strategies requires sophisticated coordination of multiple SRL processes. While various studies have proposed adaptive SRL scaffolds (i.e. real-time advice on adopting effective SRL processes) and embedded them in CBLEs to facilitate learners' effective use of SRL strategies, two key research gaps remain. First, there is a lack of research on SRL scaffolds that are based on continuous assessment of both learners' SRL processes and learning conditions (e.g., awareness of learning resources) to provide adaptive support. Second, current analytics-based scaffolding mechanisms lack the scalability needed to effectively address multiple learning conditions. Integration of analytics of SRL with generative artificial intelligence (GenAI) can provide scalable scaffolding for real-time SRL processes and evolving conditions. Yet, empirical studies implementing and evaluating effects of this integration remain scarce. To address these limitations, we conducted a randomized control trial, assigning participants to three groups (control, process only, and process with condition groups) to investigate the effects of using GenAI to turn insights from real-time analytics about students' SRL processes and conditions into adaptive scaffolds. The results demonstrate that integrating real-time analytics with GenAI in adaptive SRL scaffolds - addressing both SRL processes and dynamic conditions - promotes more metacognitive learning patterns compared to the control and process-only groups. In addition, the learners showed varying levels of compliance with analytics-based GenAI scaffolds, and this was also reflected in how the learners coordinated their SRL processes, particularly in the performance phase of SRL. This study contributes to the literature by designing, implementing, and evaluating the impact of adaptive scaffolds on learners' SRL processes using real-time analytics with GenAI.

To meet the growing demand for future professionals who can present information to an audience and create quality written products, educators are increasingly assigning writing assignments that require students to gather information from multiple sources, reorganise and reinterpret knowledge from source materials, and plan for rhetorical structure goals in order to meet the task requirements. When evaluating an essay coherence, scorers manually look for the presence of required rhetorical categories, which takes time. Supervised Machine Learning (ML) techniques have proven to be an effective tool for automatic detection of rhetorical categories that approximate students’ cognitive engagement with source information. Previous studies that addressed this problem used relatively small datasets and reported relatively low kappa scores for accuracy, limiting the use of such models in real-world scenarios. Moreover, to empower educators to effectively evaluate the overall quality of students’ writing, the associations between the sequential patterns of rhetorical categories in students’ writing and writing performance must be examined, which remains largely unexplored in educational domain. Therefore, to fill these gaps, our study aimed to i) investigate the impact of data augmentation approaches on the performance of deep learning algorithms in classifying rhetorical categories in student essays according to Bloom‘s taxonomy ii) and explore the sequential patterns of rhetorical categories in students’ writing that can influence writing performance. Our findings showed that deep learning-based model BERT on Easy Data Augmentation (EDA) based augmented data achieved 20% higher Cohen’s kappa than normal (non-augmented) data, and we discovered that students in different performance groups were statistically different in terms of rhetorical patterns. Our proposed study is valuable in terms of building a data analytic foundation that can be used to create formative feedback on students’ writings based on the patterns of rhetorical categories to improve essay quality.

A sophisticated grasp of self-regulated learning (SRL) skills has become essential for learners in computer-based learning environment (CBLE). One aspect of SRL is the plan-making process, which, although emphasized in many SRL theoretical frameworks, has attracted little research attention. Few studies have investigated the extent to which learners complied with their planned strategies, and whether making a strategic plan is associated with actual strategy use. Limited studies have examined the role of prior knowledge in predicting planned and actual strategy use. In this study, we developed a CBLE to collect trace data, which were analyzed to investigate learners’ plan-making process and its association with planned and actual strategy use. Analysis of prior knowledge and trace data of 202 participants indicated that 1) learners tended to adopt strategies that significantly deviated from their planned strategies, 2) the level of prior knowledge was associated with planned strategies, and 3) neither the act of plan-making nor prior knowledge predicted actual strategy use. These insights bear implications for educators and educational technologists to recognise the dynamic nature of strategy adoption and to devise approaches that inspire students to continually revise and adjust their plans, thereby strengthening SRL.

Educational feedback has been widely acknowledged as an effective approach to improving student learning. However, scaling effective practices can be laborious and costly, which motivated researchers to work on automated feedback systems (AFS). Inspired by the recent advancements in the pre-trained language models (e.g., ChatGPT), we posit that such models might advance the existing knowledge of textual feedback generation in AFS because of their capability to offer natural-sounding and detailed responses. Therefore, we aimed to investigate the feasibility of using ChatGPT to provide students with feedback to help them learn better. Our results show that i) ChatGPT is capable of generating more detailed feedback that fluently and coherently summarizes students' performance than human instructors; ii) ChatGPT achieved high agreement with the instructor when assessing the topic of students' assignments; and iii) ChatGPT could provide feedback on the process of students completing the task, which might benefit students developing learning skills.

Essay writing has become one of the most common learning tasks assigned to students enrolled in various courses at different educational levels, owing to the growing demand for future professionals to effectively communicate information to an audience and develop a written product (i.e. essay). Evaluating a written product requires scorers who manually examine the existence of rhetorical categories, which is a time-consuming task. Machine Learning (ML) approaches have the potential to alleviate this challenge. As a result, several attempts have been made in the literature to automate the identification of rhetorical categories using Rhetorical Structure Theory (RST). However, RST do not provide information regarding students’ cognitive level, which motivates the use of Bloom’s Taxonomy. Therefore, in this research we propose to: i) investigate the extent to which classification of rhetorical categories can be automated based on Bloom’s taxonomy by comparing the traditional ML classifiers with the pre-trained language model BERT, ii) explore the associations between rhetorical categories and writing performance. Our results showed that BERT model outperformed the traditional ML-based classifiers with 18% better accuracy, indicating it can be used in future analytics tool. Moreover, we found a statistical difference between the associations of rhetorical categories in low-achiever, medium-achiever and high-achiever groups which implies that rhetorical categories can be predictive of writing performance.