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Successful transition from secondary to higher education using learning analytics

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Abstract

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.
44th SEFI Conference, 12-15 September 2016, Tampere, Finland
Successful transition from secondary to higher education using
learning analytics
Tinne De Laet, Tom Broos, Jan-Paul van Staalduinen, Philipp Leitner, Martin Ebner
CONTEXT
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.
A successful transition from secondary to higher education can be characterized by different
features:
1. Academic achievement (e.g. credits obtained, GPA, timely graduation)
2. Realistic academic self-concept and expectations (e.g. correct positioning with respect to
peers)
3. Well-being, good perceived-fit, good quality of motivation, and
4. In-time re-orientation of field of study in case of wrong study choice
SUPPORTING LITERATURE
The study of Briggs et al. (2012) indicates the importance of encouragement and individual
support during the transition and targeted activities that enable learning about higher
education. The transition period from learning dependence to learning autonomy also
provides the opportunity for students to reflect on their conceptions of learning and
assessment, and how the first-year experience may allow individuals to prepare for university
study and develop key skills for their future careers (Hodgson et al. 2010). Therefore instead
of perceiving assessment during the transition as hoops to jump through, it can also be seen
as the basis of a critical environment in which students can develop confidence and become
more sophisticated learners (Hodgson et al. 2010). This however requires that each student
receives immediate and continuous feedback throughout the entire transition and learning
process. The focus of this feedback is not summative (i.e. connected to exams within the
study program) but rather comparative (i.e. positioning with respect to the peers) and
formative (i.e. feedback based on monitoring the student learning in order to improve their
learning process). Moreover, the quality of this feedback should be high. Therefore, the
feedback should not be merely automatic (e.g. based on learning analytics) but
supplemented with personalized feedback from student counsellors. Students with
disadvantaged background can especially profit from this feedback (For a detailed
explanation see next section). Foster & Lefever (2011) found that male students reported that
they were less engaged with their studies and had weaker relationships with students and
particularly academic staff. Male students were more likely to withdraw than their female
peers. This appeared to be partly because they were less aware that there might be a
problem and less aware of the support mechanisms available such as study counselling.
METHODOLOGY
By applying learning analytics on indicators that are predictive for a successful transition, the
generated individual learner profile will allow students to adapt their learning activities and
improve their self-regulation. For student counsellors, access to real-time learner profiles will
allow an earlier detection of at risk students and optimized coaching of particular learner
profiles. Therefore, the application of learning analytics to the transition of secondary to
higher education has a high potential of raising the quality of the support given to students
during this transition. For example, Arnold (2010) found that a learning analytics traffic light
system on the university VLE led to changes in student behavior. Once students became
aware that they were exhibiting behaviors that were not conducive to academic success,
they started to increase their overall engagement and became more likely to seek out help.
WORKSHOP
The workshop will present the first results of the STELA (Successful Transition from
Secondary to Higher Education through Learning Analtyics) Erasmus+ project with partners
KU Leuven, TU Delft, TU Graz, Nottingham Trent University, and SEFI. The goal of the
project is to develop, test, and assess a learning analytics approach for the specific
challenging field of transition from secondary to higher education. By applying the developed
approach to diverse educational contexts (different countries, different admission policies,
different faculties), the approach will have a clear potential for mainstreaming and for
extension to other fields than the transition. Moreover, the project will provide an in-depth
review of the effective use of learning analytics during this transition, which can be translated
to policy recommendations concerning the use of learning analytics to raise the quality in
education.
The first project results include a literature survey of the use of learning analytics for
supporting the transition from secondary to higher education, and the first results of setting
up a learning analytics approach at the main project partner and Nottingham Trent University,
who is already running a learning analytics dashboard for two years. Moreover, the workshop
will engage the participants in sharing their thoughts on opportunities, challenges, strengths
and difficulties of the use of learning analytics with the particular audience of first year
engineering students into mind. Additionally, possible routes for evaluating learning analytics
approaches will be presented and discussed upon. Finally, the focus will be on institutional
challenges (with a special focus to privacy and ethics) and policy recommendations.
CONCLUSIONS AND FUTURE WORK
The workshop will present the first project results of the Erasmus+ project and already show
the discovered challenges and opportunities for using learning analytics to support first year
engineering students. In the future the project will further focus on developing and
implementing a learning analytics approach that will be applied to three European
engineering bachelor programs, but will be transferable to other universities too.
ACKNOWLEGMENT
We gratefully acknowledge the support of the Erasmus+ program; STELA Project with
number 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD.
REFERENCES
[Arnold, 2010] Signals: Applying Academic Analytics [Homepage of EDUCAUSE Review],
[Online]. Available: http://www.educause.edu/ero/article/signals-applying-academic-
analytics#TB_inline?height=500&width=630&inlineId=sidebar2&modal=false [February/13,
2015].
[Banger 2008] Preparing High School Students for Successful Transitions to Postsecondary
Education and Employment
http://betterhighschools.org/docs/PreparingHSStudentsforTransition_073108.pdf
[Briggs et al. 2012] A.R.J. Briggs, J. Clark & I. Hall (2012): Building bridges: understanding
student transition to university, Quality in Higher Education,
DOI:10.1080/13538322.2011.614468
[Carenevale & Desrochers 2003] Standards for what? The economic roots of K–16 reform.
Princeton, NJ: Education Testing Service. Retrieved January 8, 2008, from
http://www.transitionmathproject.org/assets/docs/ resources/standards_for_what.pdf
[Foster & Lefever 2011] Barriers and strategies for retaining male students. In: L. THOMAS
and J. BERRY, eds, Male access & success in Higher Education: a discussion paper. York:
Higher Education Academy, pp. 20-25.
[Hodgson et al 2010] Hodgson P, Lam P, Chow C. (2010) Assessment experience of first-
year university students: dealing with the unfamiliar
http://www.cetl.hku.hk/conference2010/pdf/Hodgson.pdf
[Tinto 1993] Leaving College: Rethinking the Causes and Cures of Student Attrition.
Chicago: University of Chicago Press.
... Different studies pointed out how Learning Analytics can help to identify different kind of learners [16], how students remain in MOOCs [17], how gamification elements assist the learning process [18] and even how new didactical approaches, called Inverse Blended Learning, are introduced [19]. Another joint project on European level between KU Leuven, University Nottingham, TU Delft and TU Graz called STELA ("Successful Transition from secondary to higher Education using Learning Analytics") aimed to assist students during their transition phase from secondary to higher education [20]. The outcome of the project provided a general framework for building students' dashboards [21] (Leitner & Ebner, 2016) and different prototypes at each single university. ...
... Another joint project on European level between KU Leuven, University Nottingham, TU Delft and TU Graz called STELA ("Successful Transition from secondary to higher Education using Learning Analytics") aimed to assist students during their transition phase from secondary to higher education [20]. The outcome of the project provided a general framework for building students' dashboards [21] (Leitner & Ebner, 2016) and different prototypes at each single university. ...
... Lately, Some European projects have been launched, examples are the STELA project between KU Leuven, University of Nottingham, TU Delft and TU Graz (Leitner & Ebner, 2016), The LACE project (The Learning Analytics Community Exchange), SHEILA Project and the collaboration between (SLATE) at the University of Bergen and Erasmus University Rotterdam. Nonetheless, collaboration and funding on the European level are still relatively scarce. ...
Article
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... Another project, "STELA" (Successful Transition from Secondary to Higher Education Using Learning Analytics), aims to ease the transition from secondary level to university. Teachers and student advisors are supported by LA to improve counselling and teaching [12]. ...
Chapter
Open online courses contribute to learning in different ways (remotely, blended), but they can also provide guidance in students’ choices. Orient@mente service at the University of Turin aims at facilitating the transition from secondary schools to higher education with an open platform that delivers automatic assessments. Students can test themselves in order to understand their capabilities and their attitude toward certain disciplines. Moreover, when appropriate, students can attend remedial courses to fill the gaps in their knowledge. Orient@mente first started in 2014, and, after years of continuous deployment, the online platform has collected many data from students interested in starting a university program. A natural question concerns the effectiveness of the action: Is there a difference between the academic results of students who practice self-assessment in Orient@mente and other university students? In order to answer, we considered the average number of ECTS acquired by university students during the first year, dividing students into two groups: those who attended Orient@mente and those who did not. We selected this measurable because national indicators evaluate the number of students who obtain more than 40 first-year ECTS. With proper joining rules, we put together data from different origins, such as platform logs and the university record system. The results of the analysis, viewed from different perspectives, confirm the positive impact of Orient@mente on the average number of ECTS and the average grade, with statistical significance.KeywordsData analyticsLearning analyticsMOOCsSecondary to tertiary transitionUniversity guidanceUniversity orientation
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