<i>“We’re Seeking Relevance”</i>: Qualitative Perspectives on the Impact of Learning Analytics on Teaching and Learning

Whilst a significant body of learning analytics research tends to focus on impact from the perspective of usability or improved learning outcomes, this paper proposes an approach based on Affordance Theory to describe awareness and intention as a bridge between usability and impact. 10 educators at 3 European institutions participated in detailed interviews on the affordances they perceive in using learning analytics to support practice in education. Evidence illuminates connections between an educator’s epistemic beliefs about learning and the purpose of education, their perception of threats or resources in delivering a successful learning experience, and the types of data they would consider as evidence in recognising or regulating learning. This evidence can support the learning analytics community in considering the proximity to the student, the role of the educator, and their personal belief structure in developing robust analytics tools that educators may be more likely to use

What Massive Open Online Course (MOOC) Stakeholders Can Learn from Learning Analytics?

Massive Open Online Courses (MOOCs) are the road that led to a revolution and a new era of learning environments. Educational institutions have come under pressure to adopt new models that assure openness in their education distribution. Nonetheless, there is still altercation about the pedagogical approach and the absolute information delivery to the students. On the other side with the use of Learning Analytics, powerful tools become available which mainly aim to enhance learning and improve learners performance. In this chapter, the development phases of a Learning Analytics prototype and the experiment of integrating it into a MOOC platform, called iMooX will be presented. This chapter explores how MOOC Stakeholders may benefit from Learning Analytics as well as it reports an exploratory analysis of some of the offered courses and demonstrate use cases as a typical evaluation of this prototype in order to discover hidden patterns, overture future proper decisions and to optimize learning with applicable and convenient interventions.Comment: Learning, Design, and Technology, Springer International Publishing. (pp. 1-30) (2016

IoT-based students interaction framework using attention-scoring assessment in eLearning

Students’ interaction and collaboration using Internet of Things (IoT) based interoperable infrastructure is a convenient way. Measuring student attention is an essential part of educational assessment. As new learning styles develop, new tools and assessment methods are also needed. The focus of this paper is to develop IoT-based interaction framework and analysis of the student experience of electronic learning (eLearning). The learning behaviors of students attending remote video lectures are assessed by logging their behavior and analyzing the resulting multimedia data using machine learning algorithms. An attention-scoring algorithm, its workflow, and the mathematical formulation for the smart assessment of the student learning experience are established. This setup has a data collection module, which can be reproduced by implementing the algorithm in any modern programming language. Some faces, eyes, and status of eyes are extracted from video stream taken from a webcam using this module. The extracted information is saved in a dataset for further analysis. The analysis of the dataset produces interesting results for student learning assessments. Modern learning management systems can integrate the developed tool to take student learning behaviors into account when assessing electronic learning strategies

Anterior Access to the Cervicothoracic Junction via Partial Sternotomy: A Clinical Series Reporting on Technical Feasibility, Postoperative Morbidity, and Early Surgical Outcome

Surgical access to the cervicothoracic junction (CTJ) is challenging. The aim of this study was to assess technical feasibility, early morbidity, and outcome in patients undergoing anterior access to the CTJ via partial sternotomy. Consecutive cases with CTJ pathology treated via anterior access and partial sternotomy at a single academic center from 2017 to 2022 were retrospectively reviewed. Clinical data, perioperative imaging, and outcome were assessed with regards to the aims of the study. A total of eight cases were analyzed: four (50%) bone metastases, one (12.5%) traumatic instable fracture (B3-AO-Fracture), one (12.5%) thoracic disc herniation with spinal cord compression, and two (25%) infectious pathologic fractures from tuberculosis and spondylodiscitis. The median age was 49.9 years (range: 22–74 y), with a 75% male preponderance. The median Spinal Instability Neoplastic Score (SINS) was 14.5 (IQR: 5; range: 9–16), indicating a high degree of instability in treated cases. Four cases (50%) underwent additional posterior instrumentation. All surgical procedures were performed uneventfully, with no intraoperative complications. The median length of hospital stay was 11.5 days (IQR: 9; range: 6–20), including a median of 1 day in an intensive care unit (ICU). Two cases developed postoperative dysphagia related to stretching and temporary dysfunction of the recurrent laryngeal nerve. Both cases completely recovered at 3 months follow-up. No in-hospital mortality was observed. The radiological outcome was unremarkable in all cases, with no case of implant failure. One case died due to the underlying disease during follow-up. The median follow-up was 2.6 months (IQR: 23.8; range: 1–45.7 months). Our series indicates that the anterior approach to the cervicothoracic junction and upper thoracic spine via partial sternotomy can be considered an effective option for treatment of anterior spinal pathologies, exhibiting a reasonable safety profile. Careful case selection is essential to adequately balance clinical benefits and surgical invasiveness for these procedures

Supporting academic decision making at higher educational institutions using machine learning-based algorithms

Decisions made by deans and university managers greatly impact the entire academic community as well as society as a whole. In this paper, we present survey results on which academic decisions they concern and the variables involved in them. Using machine learning algorithms, we predicted graduation rates in a real case study to support decision making. Real data from five undergraduate engineering programs at District University Francisco Jose de Caldas in Colombia illustrate our results. The comparison between support vector machine and artificial neural network is held using the confusion matrix and the receiver operating characteristic curve. The algorithm methods and architecture are presented

Give Me a Customizable Dashboard: Personalized Learning Analytics Dashboards in Higher Education

© 2017 Springer Science+Business Media B.V. With the increased capability of learning analytics in higher education, more institutions are developing or implementing student dashboards. Despite the emergence of dashboards as an easy way to present data to students, students have had limited involvement in the dashboard development process. As part of a larger program of research examining student and academic perceptions of learning analytics, we report here on work in progress exploring student perceptions of dashboards and student preferences for dashboard features. First, we present findings on higher education students’ attitudes towards learning analytic dashboards resulting from four focus groups (N = 41). Thematic analysis of the focus group transcripts identified five key themes relating to dashboards: ‘provide everyone with the same learning opportunities’, ‘to compare or not to compare’, ‘dashboard privacy’, ‘automate alerts’ and ‘make it meaningful—give me a customizable dashboard’. Next we present findings from a content analysis of students’ drawings of dashboards demonstrating that students are interested in features that support learning opportunities, provide comparisons to peers and are meaningful to the student. Finally, we present preliminary findings from a survey of higher education students, reinforcing students’ desire to choose whether to have a dashboard and to be able to customize their dashboards. These findings highlight the potential for providing students with some level of control over learning analytics as a means to increasing self-regulated learning and academic achievement. Future research directions aimed at better understanding students emotional and behavioral responses to learning analytics feedback on dashboards and alerts are outlined

Recommending insightful drill-downs based on learning processes for learning analytics dashboards

Learning Analytics Dashboards (LADs) make use of rich and complex data about students and their learning activities to assist educators in understanding and making informed decisions about student learning and the design and improvement of learning processes. With the increase in the volume, velocity, variety and veracity of data on students, manual navigation and sense-making of such multi-dimensional data have become challenging. This paper proposes an analytical approach to assist LAD users with navigating the large set of possible drill-down actions to identify insights about learning behaviours of the sub-cohorts. A distinctive feature of the proposed approach is that it takes a process mining lens to examine and compare students’ learning behaviours. The process oriented approach considers the flow and frequency of the sequences of performed learning activities, which is increasingly recognised as essential for understanding and optimising learning. We present results from an application of our approach in an existing LAD using a course with 875 students, with high demographic and educational diversity. We demonstrate the insights the approach enables, exploring how the learning behaviour of an identified sub-cohort differs from the remaining students and how the derived insights can be used by instructors