Input from stakeholders for a Learning Analytics for Learning Design tool
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ECTEL 2018 Presentation: Make it Personal - Gathering input from stakeholders for a Learning Analytics supported Learning Design tool. Leeds 6-9-2018
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Input from stakeholders for a Learning Analytics for Learning Design tool
- 1. “Make it Personal!” – Gathering input from stakeholders for a Learning Analytics-supported Learning Design tool Marcel Schmitz1, Maren Scheffel2, Evelien van Limbeek1, Roger Bemelmans1, and Hendrik Drachsler2,3,4 1 Zuyd University of Applied Sciences, NL 2 Open University, NL 3 Goethe University Frankfurt, DE 4 German Institute for International Educational Research (DIPF), DE
- 3. Learning Activity Lecture Learning Activity Workshop Learning Activity Online Discussion Learning Activity Learning Activity Learning Activity Lecture Learning Activity Workshop Learning Activity Online Discussion Learning Activity Learning Activity Current situation Learning Design Actions after analysis 3 Behavior during Learning Analysis Learning Analytics Dashboards
- 4. Metacognitive Competences Learning Activity Lecture Learning Activity Workshop Learning Activity Online Discussion Learning Activity Learning Activity Learning Activity Lecture Learning Activity Workshop Learning Activity Online Discussion Learning Activity Learning Activity Idea Desired Situation LA4LD Design Time Run Time Learning Design Actions after analysis 4 Behavior during Learning Analysis Learning Analytics Dashboards Actions after analysis
- 5. Design based Research Main research Question: Can Learning Analytics, by use of Learning Dashboards, improve the Learning Design of a course during run-time and in that way make the learning process more efficient, effective or increase the satisfaction of teachers and students? Research Type: Design Based Research Substudy 1: State of the art, Identification context, users Substudy 2: Concept/Tool design – “Run Time” Substudy 3: Tool (re)design – “Metacognitive competences” Substudy 4: Interventions/actions? 5
- 6. State of the art 6 Schmitz, M., Limbeek van, E., Greller, W., Sloep, P., Drachsler, H.: Opportunities and challenges in using learning analytics in learning design. In: Data Driven Approaches in Digital Education: Proceeding of 12th European Conference on Technology Enhanced Learning, EC-TEL pp. 209–223 (2017).
- 7. Identification context and users. • Experiment learning dashboard (SURF-LAD) • Comparison online activity, student learning strategies and motivation and results. (REFLECTOR) Schmitz, M., Scheffel, M., Limbeek van, E., Halem van, N., Cornelisz, I., Klaveren van, C., Bemelmans, R., Drachsler, H.: Investigating the relationships between online activity, learning strategies and grades to create learning analytics-supported learning designs. • Survey on data and dashboard perspective • Focus groups/Creative sessions on data and dashboard perspective with students, teachers and IT support staff. 7 Schmitz, M., Scheffel, M., Limbeek van, E., Bemelmans, R., Drachsler, H.: “Make it personal!”- Gathering input from stakeholders for a Learning Analytics-supported Learning Design tool.
- 8. Identification context and users Research Questions 1. What do teachers want to see displayed in their LA4LD tool? 2. What do students want to see displayed in their LA4LD tool? 3. What is the current state of self-reflection from the students? 8
- 9. Methods (Focusgroups Data) Three focusgroups Data Students (A1) and teachers (A2) • Learning analytics beneficial? • Only hard data (login, download and online presence data) or also soft data like (emotions and learning styles)? • Anonymous? • Imagine Learning Analytics in any of your courses now? • Current insight in your progress now? 9
- 10. Methods (Focusgroups Data) Three focusgroups Data Guiding questions: TEL experts (A3) • Which learning tools are being used? • Can those tools/data be connected? • If not, why not? • Policy, rules or legislations? • Are there analytics systems? 10
- 11. Methods (Focusgroups Dashboard) 11 Three focusgroups Dashboard Discussion guided on elements of d.School design process • Insight cards • Brainstorm • Create Students 1th year (B1) Students >1th year (B2) and teachers (B3)
- 12. Methods (Survey – 575 students asked) Motivated Strategies for Learning Questionnaire (MSLQ) 12 Rehearsal 4 Intrinsic Goal Orientation 4 Elaboration 6 Extrinsic Goal Orientation 4 Organization 4 Task Value 6 Critical Thinking 5 Control of Learning Beliefs 4 Metacognitive self-regulation 12 Self Efficacy for Learning and Performance8 Time and Study environment 8 Test Anxiety 5 Effort Regulation 4 31 Peer Learning 3 Help Seeking 4 50 Learning Strategies Motivation Reviews selfregulated learning & instruments Panadero, E.: A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology 8, 422 (2017) Roth, A., Ogrin, S., Schmitz, B.: Assessing self-regulated learning in higher education: a systematic literature review of self-report instruments. Educational Assessment, Evaluation and Accountability 28(3), 225{250 (2016) MSLQ Handbook: Pintrich, P., Smith, D., Garcia, T., Wilbert, M.: A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ) (1991) Metacognitive self-regulation Planning 1 Scan content & structure before 2 Able to adjust behaviour 3 Decide what is needed to know before 4 Set targets to direct activities Monitoring 1 Miss information, because distracted 2 Tries using other material, if too difficult 3 Having the idea of not remembering the material 4 Tries finding out which subjects aren’t understood 5 Spend time after class, if don’t understand during Self-regulation 1 Ask self questions to filter most important information 2 Read again the material, if don’t understand 3 Ask self question to check if I understand it
- 13. Methods (Survey Data – 575 students asked) 13 Extra questions on data • Insight study progress now • Using measurement instruments in the classroom • Willingness to make online data available. • Willingness to give extra data to improve educational units • Willingness to share not anonymously • I would like to receive personal feedback on: Possible answers (more than one is accepted): • the way I learn • usage of learning material • tests results • lectures • collaboration
- 14. Results (Survey MSLQ Planning) 14 n = 143 P1: Scan material before 67 do 50 do not P3: Decide what they need to know before 67 do 48 do not P4: Set targets 48 do 66 do not P2: Change behavior 63 claim able, 39 claim not able. 41 don’t know
- 15. Results (Survey MSLQ Monitoring) 15 n = 143 M1: 92 are not easily distracted, 16 are M3: 74 are sure that they can not remember, 41 think they can M4: 82 try to see what they don't understand during study, 27 do not. M2: 67 search for other resources, 48 do not M5: 35 spends extra time, 84 will not
- 16. Results (Survey MSLQ Self-reflection) 16 n = 143 SR3: Ask themselves questions if they understand 52 do and 65 do not. SR1: Ask themselves questions to filter the important 45 do and 68 do not. SR2: 107 Do read material again if they don't understand, 17 do not.
- 17. Results (Survey data) 17 n = 143 58,7% neutral or negative about measuring in the classroom 66,4% positive about sharing online activity data 55,2% would share data non anonymously Receive information on: - Test results (69,2%) - The way they learn (55,2%) - Lectures (55,2%) - Collaboration in workgroup (49%) - Usage of learning material (46,2%)
- 18. Results (Focusgroups Dashboard) 18 Three focusgroups Dashboard Discussion guided on elements of d.School design process • Insight cards Students 1th year (B1) Students >1th year (B2) and teachers (B3)
- 19. Results (Focusgroups Dashboard) 19 Three focusgroups Dashboard Discussion guided on elements of d.School design process Students 1th year (B1) Students >1th year (B2) and teachers (B3)
- 20. Results (Focusgroups) 20 Data perspective A1: Students (n=5) A2: Teachers (n=5) Dashboard perspective B1: Students year 1 (n=6) B2: Students year >1 (n=6) B3: Teachers (n=6) A3: TEL support (n=6) Results A3: Insight in local situation
- 21. Discussion (Focusgroups) 21 • Student and teachers do not make a specific distinction between learning systems, learning design and learning analytics • Because students see learning analytics as an integrated part of their online learning environment, other functionalities can stimulate them using learning analytics (i.e.: Learning activity scheduler) • Learning activities are a central element
- 23. Discussion (Survey) 23 • There is an opportunity for improvement in students’ planning. • The students claim to do more in less time. • Students are prepared to share data. • Self reflection is low.
- 24. Conclusion 24 • Make it personal (both students and teachers) • As students and teachers see education holistic a data ecosystem is the desired solution • First year students do have different wants and needs
- 25. Future work 25 From Make it personal to Make it sensible i.e.: Students and teachers want to compare, researchers give a warning signal for comparison. Future work: Experiment timing feedback on learning design planned fall 2018. (Design time versus Run time after each activity/ Run time weekly) Jivet, I., Scheffel, M., Specht, M., Drachsler, H.: License to evaluate: Preparing learning analytics dashboards for educational practice. In: Proceedings of the 8th International Conference on Learning Analytics and Knowledge. pp. 31{40. ACM, New York, NY, USA (2018)