University students’ self-regulated learning using digital technologies

Abstract Analysing the process by which students—whether at university or not—manage and facilitate their own learning has been a recurrent educational research problem. Recently, the question arises about how the development of strategies taking place during the aforementioned process could be made easier by using technologies. In an effort to know whether university students really use digital technologies to plan, organize and facilitate their own learning, we have proposed three research questions. Which technologies do university students use to self-regulate their learning? What self-regulated learning strategies do they develop using technologies? What profiles could be identified among students based on their use of self-regulation strategies with technology? To answer these questions, the “Survey of Self-regulated Learning with Technology at the University” was designed. Information from a sample group with 711 students from various universities located in the region of Andalusia (Spain) was collected with this survey. The results indicate that university students, even when they are frequent users of digital technology, they tend not to use these technologies to regulate their own learning process. Of all technologies analysed, Internet information search and instant communication tools are used continually. In turn, the most generalised self-regulation learning strategies are those relative to social support. Nevertheless, students differ from each other regarding their use and frequency. There are groups of students who make use of self-regulation strategies when learning with technologies. In this regard, two distinctive groups of students have been identified, who show differentiated self-regulated levels

Two sides of the same coin: video annotations and in-video questions for active learning

Video in education has become pervasive. Globally, educators are recording instructional videos to augment their students’ learning and, in many contexts, replace face-to-face lectures. However, the mere act of watching a video is primarily a passive learning experience likely leading to lack of student engagement hindering learning. Active learning strategies such as video annotations and in-video questions have the potential to shift the passive experience of watching an instructional video to a more active one by engaging students with learning strategies designed to promote self-regulated learning and improve content knowledge. This experimental study investigates the impact of in-video questions compared to video annotations on learning and self-efficacy in an experimental setting. Findings revealed that learners who annotated videos had higher self-efficacy than those who completed in-video questions likely due to the immediate feedback received from the in-video questions. The study further concluded that prior knowledge plays a critical role in selecting appropriate active learning strategies, suggesting that video annotations be considered when students have prior knowledge about a topic whereas in-video questions with immediate feedback be interspersed in videos when students do not have prior knowledge about a topic

LATUX: an Iterative Workflow for Designing, Validating and Deploying Learning Analytics Visualisations

Designing, validating, and deploying learning analytics tools for instructors or students is a challenge that requires techniques and methods from different disciplines, such as software engineering, human–computer interaction, computer graphics, educational design, and psychology. Whilst each has established its own design methodologies, we now need frameworks that meet the specific demands of the cross-disciplinary space defined by learning analytics are needed. In particular, LAK needs a systematic workflow for creating tools that truly underpin the learning experience. In this paper, we present a set of guiding principles and recommendations derived from the LATUX workflow. This is a five-stage workflow to design, validate, and deploy awareness interfaces in technology-enabled learning environment. LATUX is based on well-established design processes for creating, testing, and re-designing user interfaces. We extend existing approaches by integrating the pedagogical requirements needed to guide the design of learning analytics visualizations that can inform pedagogical decisions or intervention strategies. We illustrate LATUX in a case study of a classroom with collaborative activities. Finally, the paper proposes a research agenda to support designers and implementers of learning analytics interfaces

Blended Learning Innovations: Leadership and Change in One Institution

This paper reports on the current experience of one higher education institution in Australia embarking on the path towards mainstreaming online learning opportunities by providing three complementary academic development initiatives that can inform strategies undertaken by other institutions internationally. First, an academic development program was redesigned and delivered in blended mode to provide teaching staff with the experience of learning in a blended environment to raise their awareness of effective strategies. Second, an accredited postgraduate course for teaching staff on the subject of educational design was redesigned to focus on strategies for online and blended course design and delivered fully online to raise awareness of online learning benefits. Third, a Massive Open Online Course (MOOC), entitled Learning to Teach Online (LTTO), was developed to offer professional development opportunities to teaching staff at the higher education institution, as well as to a wider international audience of educators. The threefold professional development strategies reported in this paper provide teaching staff with an opportunity to interact, mentor, and share knowledge with one another, alongside experiencing online and blended learning to effectively meet the challenge of improving the digital literacy of teaching staff and enhancing effective online and blended learning opportunities for students

Generating actionable predictive models of academic performance

© 2016 Copyright held by the owner/author(s). The pervasive collection of data has opened the possibility for educational institutions to use analytics methods to improve the quality of the student experience. However, the adoption of these methods faces multiple challenges particularly at the course level where instructors and students would derive the most benefit from the use of analytics and predictive models. The challenge lies in the knowledge gap between how the data is captured, processed and used to derive models of student behavior, and the subsequent interpretation and the decision to deploy pedagogical actions and interventions by instructors. Simply put, the provision of learning analytics alone has not necessarily led to changing teaching practices. In order to support pedagogical change and aid interpretation, this paper proposes a model that can enable instructors to readily identify subpopulations of students to provide specific support actions. The approach was applied to a first year course with a large number of students. The resulting model classifies students according to their predicted exam scores, based on indicators directly derived from the learning design

Semi-automated Student Feedback and Theory-Driven Video-Analytics: An Exploratory Study on Educational Value of Videos

Learning Analytics (LA) is a relatively novel method for automated data collection and analysis with promising opportunities to improve teaching and learning processes, widely used in educational research and practice. Moreover, with the elevated use of videos in teaching and learning processes the importance of the analysis of video data increases. In turn, video analytics presents us with opportunities as well as challenges. However, to make full use of its potential often additional data is needed from multiple other sources. On the other hand, existing data also requires context and design-awareness for the analysis. Based on the existing landscape in LA, namely in video-analytics, this article presents a proof-of-concept study connecting cognitive theory-driven analysis of videos and semi-automated student feedback to enable further inclusion of interaction data and learning outcomes to inform video design but also to build teacher dashboards. This paper is an exploratory study analysing relationship between semi-automated student feedback (on several scales on the perceived educational value of videos), video engagement, video duration and theory-driven video annotations. Results did not indicate a significant relationship between different video designs and student feedback; however, findings show some correlation between the number of visualisations and video designs. The results can have design implications as well as inform the researchers and practitioners in the field