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.

Writing from multiple sources is a commonly administered learning task across educational levels and disciplines. In this task, learners are instructed to comprehend information from source documents and integrate it into a coherent written composition to fulfil the assignment requirements. Even though educationally potent, multi-source writing tasks are considered challenging to many learners, in particular because many learners underuse monitoring and control, critical metacognitive processes for productive engagement in multi-source writing. To understand these processes, we conducted a laboratory study involving 44 university students. They engaged in multi-source writing task hosted in digital learning environment. Adding to previous research, we unobtrusively measured metacognitive processes using learners’ trace data collected via multiple data channels and in both writing and reading space of the multi-source writing task. We further investigated how these processes affect the quality of a written product, i.e., essay score. In the analysis, we utilised both automatically and human-generated essay score. The rating performance of the essay scoring algorithm was comparable to that of human raters. Our results largely support the theoretical assumptions that engagement in metacognitive monitoring and control benefits the quality of written product. Moreover, our results can inform the development of analytics-based tools that support student writing by making use of trace data and automated essay scoring.

Self-regulated learning (SRL) skills are essential for successful learning in a technology-enhanced learning environment. Learning Analytics techniques have shown a great potential in identifying and exploring SRL strategies from trace data in various learning environments. However, these strategies have been mainly identified through analysis of sequences of learning actions, and thus interpretation of the strategies is heavily task and context dependent. Further, little research has been done on the association of SRL strategies with different influencing factors or conditions. To address these gaps, we propose an analytic method for detecting SRL strategies from theoretically supported SRL processes and applied the method to a dataset collected from a multi-source writing task. The detected SRL strategies were explored in terms of their association with the learning outcome, internal conditions (prior-knowledge, metacognitive knowledge and motivation) and external conditions (scaffolding). The study results showed our analytic method successfully identified three theoretically meaningful SRL strategies. The study results revealed small effect size in the association between the internal conditions and the identified SRL strategies, but revealed a moderate effect size in the association between external conditions and the SRL strategy use.

Academic analytics focuses on collecting, analysing and visualising educational data to generate institutional insights and improve decision-making for academic purposes. However, challenges that arise from navigating a complex organisational structure when introducing analytics systems have called for the need to engage key stakeholders widely to cultivate a shared vision and ensure that implemented systems create desired value. This paper presents a study that takes co-design steps to identify design needs and strategic approaches for the adoption of academic analytics, which serves the purpose of enhancing the measurement of educational quality utilising institutional data. Through semi-structured interviews with 54 educational stakeholders at a large research university, we identified particular interest in measuring student engagement and the performance of courses and programmes. Based on the observed perceptions and concerns regarding data use to measure or evaluate these areas, implications for adoption strategy of academic analytics, such as leadership involvement, communication, and training, are discussed.

One of the ultimate goals of several learning analytics (LA) initiatives is to close the loop and support students' and teachers' reflective practices. Although there has been a proliferation of end-user interfaces (often in the form of dashboards), various limitations have already been identified in the literature such as little account for sensemaking needs. This paper addresses these limitations by proposing a question-driven LA design approach to ensure that end-user LA interfaces explicitly address teachers' questions. We illustrate this in the context of synchronous online activities orchestrated by pairs of teachers using audio-visual and text-based tools (Zoom and Google Docs). This led to the design of an open-source monitoring tool to be used in real-time by teachers when students work collaboratively in breakout rooms, and across learning spaces.

Investigation of learning tactics and strategies has received increasing attention by the Learning Analytics (LA) community. While previous research efforts have made notable contributions towards identifying and understanding learning tactics from trace data in various blended and online learning settings, there is still a need to deepen our understanding about learning processes that are activated during the enactment of distinct learning tactics. In order to fill this gap, we propose a learning analytic approach to unveiling and comparing self-regulatory processes in learning tactics detected from trace data. Following this approach, we detected four learning tactics (Reading with Quiz Tactic, Assessment and Interaction Tactic, Short Login and Interact Tactic and Focus on Quiz Tactic) as used by 728 learners in an undergrad course. We then theorised and detected five micro-level processes of self-regulated learning (SRL) through an analysis of trace data. We analysed how these micro-level SRL processes were activated during enactment of the four learning tactics in terms of their frequency of occurrence and temporal sequencing. We found significant differences across the four tactics regarding the five micro-level SRL processes based on multivariate analysis of variance and comparison of process models. In summary, the proposed LA approach allows for meaningful interpretation and distinction of learning tactics in terms of the underlying SRL processes. More importantly, this approach shows the potential to overcome the limitations in the interpretation of LA results which stem from the context-specific nature of learning. Specifically, the study has demonstrated how the interpretation of LA results and recommendation of pedagogical interventions can also be provided at the level of learning processes rather than only in terms of a specific course design.

The conceptualisation of self-regulated learning (SRL) as a process that unfolds over time has influenced the way in which researchers approach analysis. This gave rise to the use of process mining in contemporary SRL research to analyse data about temporal and sequential relations of processes that occur in SRL. However, little attention has been paid to the choice and combinations of process mining algorithms to achieve the nuanced needs of SRL research. We present a study that 1) analysed four process mining algorithms that are most commonly used in the SRL literature – Inductive Miner, Heuristics Miner, Fuzzy Miner, and pMineR; and 2) examined how the metrics produced by the four algorithms complement each. The study looked at micro-level processes that were extracted from trace data collected in an undergraduate course (N=726). The study found that Fuzzy Miner and pMineR offered better insights into SRL than the other two algorithms. The study also found that a combination of metrics produced by several algorithms improved interpretation of temporal and sequential relations between SRL processes. Thus, it is recommended that future studies of SRL combine the use of process mining algorithms and work on new tools and algorithms specifically created for SRL research.

The adoption of innovative online teaching tools in Computer Science (CS) courses provides opportunities for data-informed instruction as a regular teaching practice in CS classrooms. In this paper, we present a design study for an interactive visual analytics dashboard, called UserFlow, that supports feedback collection from teaching documents and assists instructors in interpreting feedback and acting on it in a timely manner. The design study is conducted with eight domain experts comprising of four teaching instructors, two learning analytics (LA) experts and two instructional designers. UserFlow offers a set of novel visualization designs for presenting the four interleaving aspects of document engagement (i.e., annotations, document traversal path, reading/focus time and student information). We evaluated UserFlow in an undergraduate computer science course with over 700 students. Our results demonstrate the usefulness and need for such a tool for CS educators to inform teaching approaches and courseware improvement.

In this paper, we illustrate the successful implementation of a social annotation tool within a content authoring platform, that allows students to discuss learning material with their fellow classmates, and to self-report on their cognitive, metacognitive and affective states-by self-coding the annotations as they journey through the learning material. We explore the predictive potential of such self- reports in reading material against the students completion rate and assessment scores, and also examine how visualisation of these annotation classifications can help instructors easily identify issues and adapt their teaching approach and learning material.

Learning has always been considered a social and collaborative activity. From the social constructivism perspective, students learn through the process of sharing experiences and build knowledge and understanding through discussion. This pilot study demonstrates the successful integration of an online social annotation tool in a Python programming workshop that allows students to discuss learning material and code segments with their fellow classmates. We investigate how the students leveraged the tool to improve their learning and to log their sentiments while reading the material and attempting the programming activities. We find that those students who had access to the annotation tool spent an above average amount of time on the material. They also had a higher completion rate and performance compared to the control group who did not have the annotation feature available to them. Feedback from the students who used the tool was very positive. Finally, we delve deeper into analysing the annotation patterns and explore how instructors can make use of fine-grained annotation data to inform and adapt the design of CS teaching and learning materials in online or blended contexts.