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![WHAT IS LEARNING ANALYTICS?
no universally agreed definition
7
“the measurement, collection, analysis and reporting of data about learners and their
contexts, for purposes of understanding and optimizing learning and the environments in
which it occurs” [1]
[1] Learning and Academic Analytics, Siemens, G., 5 August 2011, http://www.learninganalytics.net/?p=131
[2] What is Analytics? Definition and Essential Characteristics, Vol. 1, No. 5. CETIS Analytics Series, Cooper, A.,
http://publications.cetis.ac.uk/2012/521
“the process of developing actionable insights through problem definition and the
application of statistical models and analysis against existing and/or simulated future
data” [2]](https://image.slidesharecdn.com/efye2017kuleuvenlearningandstudystrategiespresentation-170704080545/75/Learning-and-study-strategies-a-learning-analytics-approach-for-feedback-7-2048.jpg)
![WAT IS LEARNING ANALYTICS?
8
[3] Learning Analytics and Educational Data Mining, Erik Duval’s Weblog, 30 January 2012,
https://erikduval.wordpress.com/2012/01/30/learning-analytics-and-educational-data-mining/
“learning analytics is about collecting
traces that learners leave behind and
using those traces to improve learning”
[Erik Duval, 3]
† 12 March 2016
no universally agreed definition](https://image.slidesharecdn.com/efye2017kuleuvenlearningandstudystrategiespresentation-170704080545/75/Learning-and-study-strategies-a-learning-analytics-approach-for-feedback-8-2048.jpg)
![IS IT ABOUT INSTITUTIONAL DATA?
•high-level figures:
provide an overview for internal and external reports;
used for organisational planning purposes.
•academic analytics:
figures on retention and success, used by the institution to assess performance.
•educational data mining:
searching for patterns in the data.
•learning analytics:
use of data, which may include ‘big data’,
to provide actionable intelligence for learners and teachers.
[4] Learning analytics FAQs, Rebecca Ferguson, Slideshare,
http://www.slideshare.net/R3beccaF/learning-analytics-fa-qs](https://image.slidesharecdn.com/efye2017kuleuvenlearningandstudystrategiespresentation-170704080545/75/Learning-and-study-strategies-a-learning-analytics-approach-for-feedback-9-2048.jpg)
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Learning and study strategies: a learning analytics approach for feedback
- 1. LEARNING AND STUDYSTRATEGIES A LEARNING ANALYTICS APPROACH FOR FEEDBACK Tinne De Laet KU Leuven, Belgium
- 2. OUTLINE 1. Who amI? Why I am here? 2. What is learning analytics? 3. Specific context & methodology 4. Results 5. Conclusion & reflections
- 3. WHO AM I?WHY I AM HERE?
- 4. WHO AM I?WHY I AM HERE? woman engineer Head Tutorial Services Engineering Science KU Leuven, Belgium . Tinne De Laet associate professor
- 5. WHO AM I?WHY I AM HERE? Head of Tutorial Services of Engineering Science KU Leuven • Daily experiences of challenges in transition from secondary to higher education • looking for opportunities for cross-fertilization between “first-year experience” and “engineering” •Forward-looking cooperation project: 562167- EPP-1-2015-1-BE-EPPKA3-PI-FORWARD •Successful Transition from secondary to higher Education using Learning Analytics •KU Leuven (Belgium), TU Delft (Netherlands), TU Graz (Austria), Nottingham Trent University (UK), European Society of Engineering Education (SEFI) • http://stela-project.eu/ Coordinator of STELA Erasmus+ forward-looking cooperation project
- 6. WHAT IS LEARNINGANALYTICS?
- 7. WHAT IS LEARNINGANALYTICS? no universally agreed definition 7 “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” [1] [1] Learning and Academic Analytics, Siemens, G., 5 August 2011, http://www.learninganalytics.net/?p=131 [2] What is Analytics? Definition and Essential Characteristics, Vol. 1, No. 5. CETIS Analytics Series, Cooper, A., http://publications.cetis.ac.uk/2012/521 “the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data” [2]
- 8. WAT IS LEARNINGANALYTICS? 8 [3] Learning Analytics and Educational Data Mining, Erik Duval’s Weblog, 30 January 2012, https://erikduval.wordpress.com/2012/01/30/learning-analytics-and-educational-data-mining/ “learning analytics is about collecting traces that learners leave behind and using those traces to improve learning” [Erik Duval, 3] † 12 March 2016 no universally agreed definition
- 9. IS IT ABOUTINSTITUTIONAL DATA? •high-level figures: provide an overview for internal and external reports; used for organisational planning purposes. •academic analytics: figures on retention and success, used by the institution to assess performance. •educational data mining: searching for patterns in the data. •learning analytics: use of data, which may include ‘big data’, to provide actionable intelligence for learners and teachers. [4] Learning analytics FAQs, Rebecca Ferguson, Slideshare, http://www.slideshare.net/R3beccaF/learning-analytics-fa-qs
- 10. SPECIFIC CONTEXT &METHODOLOGY
- 11. SPECIFIC CONTEXT • openadmission in the Flemish (Belgium) higher education system → a substantial part of first-year students enters without the right qualifications → first-year drop-out rate of around 30% in the Faculties of Science & Technology at KU Leuven. • university invests in advising students before and throughout the first-year • readySTEMgo Erasmus+ project • paper-and-pencil questionnaires with first-year students • five academic skills as measured in the LASSI (The Learning and Study Strategies Inventory) are important for STEM study success • concentration, performance anxiety, motivation, the use of test strategies, and time management (Pinxten et al. 2016). • Results were actively disseminated to the KU Leuven faculties’ student support services and the central study advice center, who in return adapted their coaching and training based on the results. • BUT students did not receive feedback
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- 15. THE INTERVENTION • 1406first-year KU Leuven (STEM) students with full LASSI profile • 12 study programs, 4 faculties • Students received “personalized email” with invitation to the dashboard
- 16. DO STUDENTS ENTERTHE DASHBOARD? 1135 studenten “clicked through” (80,7%)
- 17. STUDENTS WITH BETTERLEARNING SKILLS GO TO THE DASHBOARD MORE 17Kruskal-Wallis test, with multiple comparison according to Dunn with a Bonferroni correction
- 18. STUDENTS WITH WORSELEARNING SKILLS VISIT THE CORRESPONDING TAB AND READ THE TIPS MORE 18Kruskal-Wallis test, with multiple comparison according to Dunn with a Bonferroni correction
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- 20. DO STUDENTS LIKEIT? The information is clear The information is useful I would like to receive more similar information
- 21.
- 22. SOME SPECIFICS • studentslike “additional” feedback • challenge to attract “right” students to the platform • once students are on the platform, the “targeted” students interact more Future • repeat intervention and extend (KU Leuven and TU Delft) • study relation with study results • qualitative analysis using focus groups and structured interviews with students
- 23. LEARNING ANALYTICS &DATA FOCUS ON DATA THAT IS AVAILABLE • A lot of data COULD be available • What IS available?
- 24. LEARNING ANALYTICS ANDACTIONABLE INSIGHTS “Female students are more successful in higher education than male students” 70% successful 60% successful so ? FEEDBACK SHOULD ALWAYS BE ACTIONABLE
- 25. LEARNING ANALYTICS ÐICS & ADOPTION & … • Integrate all available expertise DURING development • educational scientists • computer scientists & IT experts • visualization experts • PRACTITIONERS!!! (study advisors, tutors, etc.) • students & teachers •Be wary of out-of-the-box commercial solutions! • no one-size-fits-all solution • jeopardizes acceptance of students and staff • What is underlying the recommendation? → actionable! → transparency! •Ethics AND privacy are big issues! • Ethics: involve practitioners and experts • Privacy regulations can be hurdle might be opportunity for learning analytics → overview and insight in data that IS gathered! 25 USE ALL AVAILABLE EXPERTISE