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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.

Abstract Peer feedback has been widely used in computer-supported collaborative learning (CSCL) setting to improve students’ engagement with massive courses. Although the peer feedback process increases students’ self-regulatory practice, metacognition, and academic achievement, instructors need to go through large amounts of feedback text data which is much more time-consuming. To address this challenge, the present study proposes an automated content analysis approach to identify relevant categories in peer feedback based on traditional and sequence-based classifiers using TF-IDF and content-independent features. We use a data set from an extensive course (N = 231 students) in the setting of engineering higher education. In particular, a total of 2,444 peer feedback messages were analyzed. The CRF classification model based on the TF-IDF features achieved the best performance. The results illustrate that the ability to scale up the automatic analysis of peer feedback provides new opportunities for student-improved learning and improved teacher support in higher education at scale.

Self-regulated learning (SRL) can predict academic performance. Yet, it is difficult for learners. The ability to self-regulate learning becomes even more important in emerging online learning settings. To support learners in developing their SRL, learning analytics (LA), which can improve learning practice by transforming the ways we support learning, is critical. This scoping review is based on the analysis of 54 papers on LA empirical research for SRL in online learning contexts published between 2011 and 2019. The research question is: What is the current state of the applications of learning analytics to measure and support students' SRL in online learning environments? The focus is on SRL phases, methods, forms of SRL support, evidence for LA and types of online learning settings. Zimmerman's model (2002) was used to examine SRL phases. The evidence about LA was examined in relation to four propositions: whether LA i) improve learning outcomes, ii) improve learning support and teaching, iii) are deployed widely, and iv) used ethically. Results showed most studies focused on SRL parts from the forethought and performance phase but much less focus on reflection. We found little evidence for LA that showed i) improvements in learning outcomes (20%), ii) improvements in learning support and teaching (22%). LA was also found iii) not used widely and iv) few studies (15%) approached research ethically. Overall, the findings show LA research was conducted mainly to measure rather than to support SRL. Thus, there is a critical need to exploit the LA support mechanisms further in order to ultimately use them to foster student SRL in online learning environments.

Massive Open Online Courses (MOOCs) allow learning to take place anytime and anywhere with little external monitoring by teachers. Characteristically, highly diverse groups of learners enrolled in MOOCs are required to make decisions related to their own learning activities to achieve academic success. Therefore, it is considered important to support self-regulated learning (SRL) strategies and adapt to relevant human factors (e.g., gender, cognitive abilities, prior knowledge). SRL supports have been widely investigated in traditional classroom settings, but little is known about how SRL can be supported in MOOCs. Very few experimental studies have been conducted in MOOCs at present. To fill this gap, this paper presents a systematic review of studies on approaches to support SRL in multiple types of online learning environments and how they address human factors. The 35 studies reviewed show that human factors play an important role in the efficacy of SRL supports. Future studies can use learning analytics to understand learners at a fine-grained level to provide support that best fits individual learners. The objective of the paper is twofold: (a) to inform researchers, designers and teachers about the state of the art of SRL support in online learning environments and MOOCs; (b) to provide suggestions for adaptive self-regulated learning support.