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STELA

[workhop, publication] Learning dashboard for supporting students: from first-year engineering to MOOC students #stela #learninganalytics

Our workshop at this year SEFI-conference in Copenhagen about „Learning dashboard for supporting students: from first-year engineering to MOOC students“ has been published in the conference proceeding.

By applying learning analytics on indicators that are predictive for a successful transition and online course completion, students can be provided with feedback on in order to improve their self-regulation, hereby providing support during the first-year and in online courses.

[Full conference proceeding]

[Workshop description @ ResearchGate]

Reference: De Laet, T., Broos, T., van Staalduinen, J.P, Ebner, M. (2018) Learning dashboard for supporting students: from first-year engineering to MOOC students. Proceeding of 46th SEFI Conference 17-21 September 2018. pp. 1454-1456. Copenhagen, Denmark

[publication] Transferring learning dashboards to new contexts: experiences from three case studies #LearningAnalytics #STELA

Our publication about „Transferring learning dashboards to new contexts: experiences from three case studies“ at this year Open Education Global Conference in Delft got published right now.

Abstract:

This papers focuses on the use of learning dashboards in higher education to foster self-regulated learning and open education. Students in higher education have to evolve to independent and lifelong learners. Actionable feedback during learning that evokes critical self-reflection, helps to set learning goals, and strengthens self-regulation will be supportive in the process. Therefore, this paper presents three case studies of learning analytics in higher education and the experiences in transferring them from one higher education institute than the other. The learning dashboard from the three case studies is based on two common underlying principles. First, they focus on the inherent scalability and transferability of the dashboard: both considering the underlying data and the technology involved. Second, the dashboard use as underlying theoretical principles Actionable Feedback and the Social Comparison Theory. The learning dashboards from the case studies are not considered as the contribution of this paper, as they have been presented elsewhere. This paper however describes the three learning dashboards using the general framework of Greller and Drachsler (2012) to enhance understanding and comparability. For each of the case study, the actual experiences of transferability obtained within a European collaboration project (STELA, 2017) are reported. This transferability and scalability is the first-step of creating truly effective Open Educational Resources from the Learning Analtyics Feedback dashboards. The paper discusses how this collaboration impacted and transformed the institutes involved and beyond. The use of open education technology versus proprietary solutions is described, discussed, and translated in recommendations. As such the research work provides insight on how learning analytics resources could be transformed into open educational resources, freely usable in other higher education institutes.

[Link to article @ ResearchGate]

[Link to article @ Conference Proceeding Database]

Reference: De Laet, T., Broos, T., van Staalduinen, J.-P., Ebner, M., Leitner, P. (2018)Transferring learning dashboards to new contexts: experiences from three case studies. In: Conference Proceeding Open Educational Global Conference 2018. p. 14. Delft, Netherlands

[publication] Lessons Learned when transferring Learning Analytics Interventions across Institutions #lak18 #research

Our publication at this year LAK18-conference about „Lessons Learned when transferring Learning Analytics Interventions across Institutions“ is now online available.

Abstract:

Learning Analytics is a promising research field, which is advancing quickly. Therefore, it finally impacts research, practice, policy, and decision making in the field of education. Nonetheless, there are still influencing obstacles when establishing Learning Analytics initiatives on higher education level. Besides the much discussed ethical and moral concerns, there is also the matter of data privacy. In 2015, the European collaboration project STELA started with the main goal to enhance the Successful Transition from secondary to higher Education by means of Learning Analytics. Together, the partner universities develop, test, and assess Learning Analytics approaches that focus on providing feedback to students. Some promising approaches are then shared between the partner universities. Therefore, the transferability of the Learning Analytics initiatives is of great significance. During the duration of our project, we found a variety of difficulties, we had to overcome to transfer one of those Learning Analytics initiatives, the Learning Tracker from one partner to the other. Despite, some of the difficulties can be categorized as small, all of them needed our attention and were time consuming. In this paper, we present the lessons learned while solving these obstacles.

[Full paper @ LAK 2018]

Reference: Leitner, P., Broos, T. & Ebner, M. (2018) Lessons Learned when transferring Learning Analytics Interventions across Institutions. In: Companion Proceedings 8th International Conference on Learning Analytcis & Knowledge. Sydney. pp. 621-629

[publication] Confidence in and beliefs about first-year engineering student success: case study from KU Leuven, TU Delft, and TU Graz #research #STELA

One of our intellectual outputs of the STELA-project is a case study amongt our partners. We did a study about how students feel in the very first beginning of their study and published it at the SEFI-conference.

Abstract:

This paper explores the confidence freshman engineering students have in being successful in the first study year and which study-related behaviour they believe to be important to this end. Additionally, this paper studies which feedback these students would like to receive and compares it with the experiences of second-year students regarding feedback. To this end, two questionnaires were administered: one with freshman engineering students to measure their expectations regarding study success and expected feedback and one with second-year engineering students to evaluate their first year feedback experience.
The results show that starting first-year engineering students are confident regarding their study success. This confidence is however higher than the observed first-year students success. Not surprisingly, first-year students have good intentions and believe that most academic activities are important for student success. When second-year students look back on their first year, their beliefs in the importance of these activities have strongly decreased, especially regarding the importance of preparing classes and following communication through email and the virtual learning environment. First-year students expect feedback regarding their academic performance and engagement. They expect that this feedback primarily focuses on the impact on their future study pathway rather than on comparison to peer students. Second-year students indicate that the amount of feedback they receive could be improved, but agree with the first-year students that comparative feedback is less important.

[Full Article @ ResearchGate]

Reference: de Laet, T., Broos, T., van Staalduinen, J.-P., Ebner, M., Langie, G., van Soom, C. & Shepers, W (2018) Confidence in and beliefs about first-year engineering student success: case study from KU Leuven, TU Delft, and TU Graz. In: Proceedings of the 45th SEFI Conference, pp. 1-9. Azores, Portugal

[publication] Development of a Dashboard for Learning Analytics in Higher Education #STELA #LearningAnalytics

Our first publication at this year HCII 2017 conference was about „Development of a Dashboard for Learning Analytics in Higher Education“.
Abstract:

In this paper, we discuss the design, development, and implementation of a Learning Analytics (LA) dashboard in the area of Higher Education (HE). The dashboard meets the demands of the different stakeholders, maximizes the mainstreaming potential and transferability to other contexts, and is developed in the path of Open Source. The research concentrates on developing an appropriate concept to fulfil its objectives and finding a suitable technology stack. Therefore, we determine the capabilities and functionalities of the dashboard for the different stakeholders. This is of significant importance as it identifies which data can be collected, which feedback can be given, and which functionalities are provided. A key approach in the development of the dashboard is the modularity. This leads us to a design with three modules: the data collection, the search and information processing, and the data presentation. Based on these modules, we present the steps of finding a fitting Open Source technology stack for our concept and discuss pros and cons trough out the process.

[Publication @ Springer]

[Draft @ ResearchGate]

Reference: Leitner P., Ebner M. (2017) Development of a Dashboard for Learning Analytics in Higher Education. In: Zaphiris P., Ioannou A. (eds) Learning and Collabo- ration Technologies. Technology in Education. LCT 2017. Lecture Notes in Computer Science, vol 10296. pp. 293-301 Springer, Cham

[workshop, publication] Successful transition from secondary to higher education using learning analytics #STELA #LearningAnalytics

For the first time we introduced our project STELA to a broader publid at the 44th SEFI conference in Finland. In our main focus is the transition from secondary to higher education using learning anlaytics.
Introduction:

The economic and financial crisis is having an important socio-economic effect in Europe and is threatening Europe’s economic growth model and employment and the sustainability of Europe’s welfare model. To counter the crisis, Europe should further evolve to a knowledge- driven and technology-based economy. This evolution however causes a rise in the demand for personnel with post-secondary education diploma, since many jobs in such a knowledge en technology-drive economy require at least a postsecondary education (Carnevale & Desrochers 2003). However, during the transition from secondary to higher education a lot of high-potential students drop out (Banger 2008).

[Full Workshop publication @ ResearchGate]

Reference: De Laet, T, Broos, T.,van Staalduinen, J.-P., Leitner, P., Ebner, M. (2016) Successful transition from secondary to higher education using learning analytics. 44th SEFI Conference, Tampere, Finland [.pdf]