Self-regulated learning (SRL) is essential for academic success in higher education, yet many students struggle to effectively regulate their own learning behaviours. While goal-setting interventions can help students set high-quality goals as the foundation for their learning behaviours, additional supports are needed to help transition from goal setting into effective goal striving. This study examines the impact of monitoring and reflection supports, delivered through a scripted conversational agent, on students’ goal attainment, SRL skills, and academic performance. In this study, 84 undergraduate students were randomly assigned to one of four experimental conditions: Control, Monitoring Only, Reflection Only, or Monitoring & Reflection. Over a five-week intervention, participants engaged in weekly goal-setting activities, with additional monitoring and/or reflection prompts depending on their assigned condition. Results showed that participants in the Monitoring Only and Monitoring & Reflection conditions reported significantly higher goal attainment than those in the Reflection Only and Control groups, suggesting that monitoring plays a critical role in reinforcing goal-directed behaviour. While SRL skills improved across all conditions, no significant differences were found between groups, indicating that consistent goal setting alone may support SRL development. There were no significant effects of the intervention on academic performance. These findings highlight the immediate effectiveness of progress-monitoring activities and suggest that reflection may require longer intervention periods to have significant effects. This study supports the use of conversational agents for delivering scalable SRL support and provides insights into the design of multi-phase SRL supports in digital education.

Goal setting is the first and driving stage of the self-regulated learning cycle. Studies have shown that supporting goal setting is an effective means of improving academic performance among higher education students. However, doing so can be complex and resource intensive. In this study, a goal-setting conversational agent was designed and deployed to support higher education students in setting academic goals. Across 5-weeks, we tested the effects of goal-setting prompts (guided vs. unguided) and adaptive feedback (with vs. without) when delivered via a goal-setting conversational agent. We explored the effects of these supports (i.e., guidance and feedback) on students' 1) goal quality and 2) goal attainment. Findings showed that guidance and feedback combined had the largest positive effect on goal quality. They also revealed that guidance alone produced initially high-quality goals which decreased in quality overtime, whereas feedback had a delayed but cumulative effect on quality across multiple goal setting iterations. However, neither guidance nor feedback had significant effects on goal attainment, and there was no significant relationship between goal quality and attainment. This study provides insights into how a goal-setting conversational agent and adaptive feedback can be used to support the academic goal setting process for higher education students.

Behavioural engagement as a predictor of academic success hinges on the interplay between effort and time. Exploring the longitudinal development of engagement is vital for understanding adaptations in learning behaviour and informing educational interventions. However, person-oriented longitudinal studies on student engagement are scarce. Moreover, online engagement metrics are rarely grounded in theory and often result in simplified descriptions overlooking the complexity of engagement processes. This study applies a theory-based operationalization of behavioural engagement to examine the log data of 236 students in a web-based learning platform. We explored 1) whether weekly profiles based on distinct engagement patterns can be identified and 2) how students transition across profiles over time. Hierarchical clustering yielded one Inactive and six active profiles (Fast-Learners, Regular-Learners, Average-Engagement, Minimalists, Struggling-Learners, and Procrastinators). Results suggest heterogeneity in profile emergence, with effective engagement characterized by alignment with the course deadlines. Process mining revealed changes in profile membership across weeks. Profile transitions revealed relative stability among effective groups and greater fluctuation among low-time profiles. By investigating the complexity and temporality of engagement in online learning, our findings provide insights for developing personalized learning support through training artificial intelligence applications and informing learning analytics dashboards.

Asking questions is vital for learning, yet students seldom exhibit this behavior. Given the increasing presence of online classrooms in tertiary education, it is much needed to examine ways to encourage students to ask questions and increase their engagement. Despite the critical role of asking questions to enhance learning, little is known in research on promoting this behavior. Therefore, in this paper, we empirically tested the effect of nudging – a novel approach in education consisting of subtle interventions to change behavior – in online classrooms in tertiary education on question asking behavior, student engagement, and grades. In Experiment 1 (n = 1011), the teacher's virtual background prompted questions (prompt nudge), while in Experiment 2 (n = 449), the teacher set a goal for the students to ask one question per session (goal-setting nudge). We found a trend towards a positive effect of the prompt nudge on questions, but not on grades. Exploratory analyses revealed this was driven by students who already asked many questions. We found no effect of the goal-setting nudge on any measure, nor any effect in either experiment on student engagement. The findings demonstrate that the prompt nudge can be a possible useful and easy to implement tool to encourage questions in the online classroom.

Twitter is a very popular microblogging platform that has been actively used by scientific communities to exchange scientific information and to promote scholarly discussions. The present study aimed to leverage the tweet data to provide valuable insights into the development of the learning analytics field since its initial days. Descriptive analysis, geocoding analysis, and topic modeling were performed on over 1.6 million tweets related to learning analytics posted between 2010-2021. The descriptive analysis reveals an increasing popularity of the field on the Twittersphere in terms of number of users, twitter posts, and hashtags emergence. The topic modeling analysis uncovers new insights of the major topics in the field of learning analytics. Emergent themes in the field were identified, and the increasing (e.g., Artificial Intelligence) and decreasing (e.g., Education) trends were shared. Finally, the geocoding analysis indicates an increasing participation in the field from more diverse countries all around the world. Further findings are discussed in the paper.

Learning analytics dashboards (LADs) are designed as feedback tools for learners, but until recently, learners rarely have had a say in how LADs are designed and what information they receive through LADs. To overcome this shortcoming, we have developed a customisable LAD for Coursera MOOCs on which learners can set goals and choose indicators to monitor. Following a mixed-methods approach, we analyse 401 learners' indicator selection behaviour in order to understand the decisions they make on the LAD and whether learner goals and self-regulated learning skills influence these decisions. We found that learners overwhelmingly chose indicators about completed activities. Goals are not associated with indicator selection behaviour, while help-seeking skills predict learners' choice of monitoring their engagement in discussions and time management skills predict learners' interest in procrastination indicators. The findings have implications for our understanding of learners' use of LADs and their design.

Online learning environments demand learners to self-regulate their learning but many learners are poor at self-regulated learning (SRL). In this paper, two studies were conducted to examine the effect of two SRL supports, i.e., guiding goal setting and planning using an approach known as mental contrasting and implementation intentions (MCII) and prompting SRL using videos, on motivation in the form of task value and self-efficacy, SRL in the form of persistence, task strategies, self-evaluation, and self-satisfaction, student engagement, performance, and goal attainment. In Study 1, a two (MCII, no MCII) by two (prompt, no prompt) between-subjects controlled experiment was conducted in an online video-based learning environment. Results showed that learners who completed the MCII had more sustained task value and higher persistence than learners who did not receive the MCII. Study 2 was conducted in five Massive Open Online Courses where we compared three conditions: MCII, goal only, and control. Results showed that there were no significant differences in SRL activities, course engagement and performance, and goal attainment among the three conditions. Collectively, the results suggest that the task duration in which learners’ goals can be attained (e.g., within one short session or over multiple weeks) might influence the effectiveness of MCII.

1.1 Background To educate future competent engineers, it is crucial to adopt teaching and learning approaches that support students in dealing with highly complex problems [1]. One strategy is to enhance service mathematics in higher engineering education by shifting from outcome-centered to competence-centered approaches [2]. This strategy is examined and adopted in a large-scale innovation programme of mathematics education (PRIME) at TU Delft to design effective service mathematics courses in higher engineering education. As mathematics is at the core of engineering education, we will, in this workshop, explore how to create a viable and resilient educational model for developing mathematical competencies, described in the Framework of Mathematics Curricula in Engineering Education [2, 3]. Additionally, we will discuss how the development of mathematical competencies can be facilitated by leveraging technology in blended and remote learning environments. The aim of this workshop is to start a process via a living document which serves to share and create material and expertise in teaching, learning and assessing the mathematical competencies.

Cognitive load researchers have used varying subjective techniques based on rating scales to quantify experienced cognitive load. Although it is generally assumed that subjects can introspect on their cognitive processes and have no difficulty in assigning numerical values to the imposed cognitive load, little is known about how visual characteristics of the rating scales influence the validity of the cognitive load measure. In this study we look at validity of four subjective rating scales (within groups) differing in visual appearance by participants rating perceived difficulty and invested mental effort in response to working on simple and complex weekday problems. We used two numerical scales (the nine-point Likert scale most often used in Cognitive load theory research and a Visual Analogue Scale ranging between 0–100%) and two pictorial scales (a scale consisting of emoticons ranging from a relaxed blue-colored face to a stressed red-colored face and an “embodied” scale picturing nine depicted weights from 1–9 kg). Results suggest that numerical scales better reflect cognitive processes underlying complex problem solving while pictorial scales Underlying simple problem solving. This study adds to the discussion on the challenges to quantify cognitive load through various measurement methods and whether subtleties in measurements could influence research findings.