Project

Successful transition from Secondary to Higher Education using Learning Analytic (STELA)

Goal: The main goal of the project is to enhance a successful transition from secondary to higher education by means of learning analytics. To this end the project will develop, test, and assess a learning analytics approach that focuses on providing formative and summative feedback to students in the transition. On top of the development of a student dashboard, the project will develop dashboards for the student counselors and teachers, hereby disclosing a vast amount of information that can be used to improve counselling and teaching practices.

To realize this ambitious goal the project gathers a multidisciplinary team of learning analytics researchers, educational technology experts, experts in the transition from secondary to higher education, and practitioners. Thanks to this multidisciplinary team, the project will tackle all the different steps required for the application of learning analytics: data collection, data analysis, data visualization, dashboard design, dashboard development, and last but not least the actual implementation and thorough evaluation of the learning analytics approach.

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Project log

Tinne De Laet
added a research item
First-year student success in Engineering Bachelor programs is well-studied. Both traditional statistical modelling and machine learning approaches have been used to study what makes students successful. While statistical modelling helps to obtain population-wide patterns, they often fail to create accurate predictions for individual students. Predictive machine learning algorithms can create accurate predictions but often fail to create interpretable insights. This paper compares a statistical modelling and machine learning approach for predicting first-year student success. The case study focuses on first-year Bachelor of Engineering Science students from KU Leuven between 2015-2017 and relates first-semester academic achievement to prior education, learning and study strategies, effort level, and preference for time pressure.
Martin Ebner
added a research item
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.
Martin Ebner
added a research item
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.
Martin Ebner
added a research item
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.
Martin Ebner
added a research item
This article introduces the goal and activities of the LAK 2018 half-day workshop on the involvement of stakeholders for achieving learning analytics at scale. The goal of the half-day workshop is to gather different stakeholders to discuss at-scale learning analytics interventions. In particular the workshop focuses on learning analytics applications and learning dashboards that go beyond the implementation in a single course or context, but that have at least the potential for scaling across different courses, programs, and institutes. The main theme of the workshop is to explore how the involvement of different stakeholders can strengthen or hinder learning analytics at scale. The key findings, recommendations, and conclusions of the workshop will be presented in a summarizing report, which will be shaped as a SWOT analysis for stakeholder involvement for achieving learning analytics at scale.
Tom Broos
added 3 research items
Our work focuses on a multi-institutional implementation and evaluation of a Learning Analytics Dashboards (LAD) at scale, providing feedback to N=337 aspiring STEM (science, technology, engineering and mathematics) students participating in a region-wide positioning test before entering the study program. Study advisors were closely involved in the design and evaluation of the dashboard. The multi-institutional context of our case study requires careful consideration of external stakeholders and data ownership and portability issues, which gives shape to the technical design of the LAD. Our approach confirms students as active agents with data ownership, using an anonymous feedback code to access the LAD and to enable students to share their data with institutions at their discretion. Other distinguishing features of the LAD are the support for active content contribution by study advisors and LATEX type-setting of question item feedback to enhance visual recognizability. We present our lessons learnt from a first iteration in production.
In the transition from secondary to higher education, students are expected to develop a set of learning skills. This paper reports on a dashboard implemented and designed to support this development, hereby bridging the gap between Learning Analytics research and the daily practice of supporting students. To demonstrate the scalability and usefulness of the dashboard, this paper reports on an intervention with 1406 first-year students in 12 different programs. The results show that the dashboard is perceived as clear and useful. While students not accessing the dashboard have lower learning skills, they make more use of the extra remediation possibilities in the dashboard.
Learning Analytics Dashboards (LAD) provide a means to leverage data to support learners, teachers, and counselors. This paper reports on an in-depth analysis of how learners interact with a LAD. N=1,406 first-year students in 12 different study programs were invited to use a LAD to support them in their transition from secondary to higher education. The LAD provides actionable feedback about five of the learning skills assessed by the Learning and Study Strategies Inventory (LASSI): concentration, anxiety, motivation, test strategies, and time management. We logged access to and behavior within the LAD and analyzed their relationship with these learning skills. While eight out of ten students accessed the LAD, students with lower time management scores tend to have a lower click-trough rate. Once within the LAD, students with lower scores for specific learning skills are accessing the corresponding information and remediation possibilities more often. Regardless of their scores for any of the other learning skills, learners with higher motivation scores are reading the remediation possibilities for the other four learning skills more often. Gender and study program have an influence on how learners use the LAD. Our findings may help both researchers and practitioners by creating awareness about how LAD use in itself may depend on the context and profile of the learner.
Martin Ebner
added a research item
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.
Tinne De Laet
added an update
Tinne De Laet
added 32 project references
Tinne De Laet
added an update
Attend the HCII 2017 workshop organized by Martin Ebner, containing two papers from our project.
 
Tinne De Laet
added an update
Check out our project fact-sheet containing the first policy recommendations: http://stela-project.eu/files/O20-factSheet_STELA_EADTU.pdf !
 
Tinne De Laet
added an update
 
Tinne De Laet
added an update
Check out the results from TU Delft's case study on the learning tracker inside MOOCs:
 
Tinne De Laet
added an update
Paper publication of project output in ICERI2016 proceedings:
 
Martin Ebner
added a research item
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). The transition to higher education is challenging both from the academic and social perspective (Tinto 1993). Firstly, students have to adjust to the life at the higher education institute, which is often totally different from living at home and going to secondary education. Secondly, the academic expectations of higher education are different than the ones from secondary education. Students have to evolve to independent learners that take their own responsibility for coping with the high study workload. This requires that first-year students in higher education have to go through a transition from learning dependence to learning autonomy.
Martin Ebner
added a research item
This chapter looks into examining research studies of the last five years and presents the state of the art of Learning Analytics (LA) in the Higher Education (HE) arena. Therefore, we used mixed-method analysis and searched through three popular libraries, including the Learning Analytics and Knowledge (LAK) conference, the SpringerLink, and the Web of Science (WOS) databases. We deeply examined a total of 101 papers during our study. Thereby, we are able to present an overview of the different techniques used by the studies and their associated projects. To gain insights into the trend direction of the different projects, we clustered the publications into their stakeholders. Finally, we tackled the limitations of those studies and discussed the most promising future lines and challenges. We believe the results of this review may assist universities to launch their own LA projects or improve existing ones.
Martin Ebner
added an update
Now publication of the project - enjoy reading :-)
 
Martin Ebner
added an update
Martin Ebner
added a project goal
The main goal of the project is to enhance a successful transition from secondary to higher education by means of learning analytics. To this end the project will develop, test, and assess a learning analytics approach that focuses on providing formative and summative feedback to students in the transition. On top of the development of a student dashboard, the project will develop dashboards for the student counselors and teachers, hereby disclosing a vast amount of information that can be used to improve counselling and teaching practices.
To realize this ambitious goal the project gathers a multidisciplinary team of learning analytics researchers, educational technology experts, experts in the transition from secondary to higher education, and practitioners. Thanks to this multidisciplinary team, the project will tackle all the different steps required for the application of learning analytics: data collection, data analysis, data visualization, dashboard design, dashboard development, and last but not least the actual implementation and thorough evaluation of the learning analytics approach.