1.
Learning Analytics with DEEDS simulator
Benefits and Challenges of Data Sharing
MehrnooshVahdat, ICE PhD student at UNIGE & TU/e
Member of LACE Project, Infinity Technology Solutions
September 16, 2014

2.
Outline
•Research plan
•Learning Analytics (LA) with DEEDS
▫Goal
▫DEEDS –a shared resource
▫Student’s learning process with DEEDS
▫LA role
•What to share?
▫Data
▫Features
•Who benefits from data sharing?
▫Challenges
•References

3.
Research plan
LA/ EDM
Higher Education
Electronic Engineering Courses: Logic Networks
Simulator
Data collection and pre- processing
Prediction of learning outcome from interaction data
Industry
Schools
Analyzing learner’s behavior

4.
Goal
•To extract the profiles of students activities, performed during the training sessions of a course of logic networks
•To relate such activities with the students’ performance at intermediate verification tests
•To explore students learning behavior on system while using Deeds simulator
•To extract non-trivial patterns from students’ interaction
•To assist instructors to be aware of students learning process

5.
DEEDS
•Stands for: Digital Electronics Education and Design Suite
•Is an interactive simulation environment for e-learning in digital electronics
•Provides learning materials
•Asks to solve varied-level problems

6.
DEEDS –A freeware and a shared resource
•Deeds is free to use for academics
•It has been and it is used now in several European universitiesand project assignments have been shared among European schools (within the European Union LeonardoDaVinciNetProproject)
•Deeds educational materials have been translated and published in English, Italian, Turkish, Spanish, Catalan.

7.
Students’ Learning Process with DEEDS Simulator Components

8.
LA role
•Prediction of students’ learning outcome from their activities in each session
•To understand which learning behavior is effective in the outcome
•To distinguish the students who need more attention in early sessions of the course
•To understand which course content/ exercise is critical
•To provide help in-time

9.
What to share?
•What data level to share concerning the data anonymity?
•Which features are critical in prediction?
•Which prediction method (to predict students’ grades) is effective for these data?

10.
Data
•Activity logs from system
•Data from the questionnaires:
▫Demographic data: consent, general, motivation, background knowledge, ICT literacy, learning style.
▫Data from students feedback
•Data from observation and semi-structured interview
•Group assessment per session
•Final grade at the end of the semester

11.
Feature ExtractionWhich features are critical in prediction?
•Samples of students’ activities :
▫Text-editor: The time students spend on writing their answers.
▫Image: Students work with images of simulation imported from the tool.
▫Circuit-simulator : Students work on an exercise with the circuit simulator.
▫Timing-diagram: Students run the circuit simulator.
▫FSM-Simulator: Students work on Finite State Machine Simulator.
▫Browser-exercise code: Students study the exercise.
▫Warning: they might have taken a wrong action.

12.
Who benefits from data sharing?
•Researchers:
▫To benefit from simulation-based critical features, and prediction methods, to develop recommendation engines integrated in digital electronics simulators
•Teachers:
▫To plan their lessons based on students’ needs and their effective activities, to help students in-need in early sessions of the course, and help them avoid most frequently mistakes.
•Students:
▫To get recommendations about activities and resources, receive more personalized help.
•DEEDS/ digital electronics simulator developers:
▫To improve simulators and adapt it to the students’ needs

13.
Challenges?
•Cost:
▫Of applications, methods to obtain meaningful data.
•Data
▫Interoperability: To bring all data levels together
▫Reliability: user’s role in activity data, trial and error or decision making?
▫Context and time: needs lots of work to make sense of unorganized information
•Ethical obligations:
▫Privacy and anonymity
(Bienkowski, Feng, & Means, 2012; del Blanco et al., 2013; Gyllstrom, 2009)

14.
These slides are provided under the Creative Commons Attribution Licence: http://creativecommons.org/licenses/by/4.0/. Some images used may have different licence terms.
Learning Analytics with Deeds simulator: Benefits and Challenges of Data Sharing
by MehrnooshVahdat
was presented at Learning Analytics Data Sharing –LADS14 Workshop at EC-TEL.
Graz -16thSeptember 2014
mehrnoosh.vahdat@edu.unige.it
http://goo.gl/ouywVU
@MehrnooshV

15.
References
•Baker, R.S., Corbett, A.T., Koedinger, K.R. (2004). Detecting student misuse of intelligent tutoring systems
•Baker, S.J.d., Yacef, K. (2009) The State of Educational Data Mining in 2009: A Review and Future Visions.
•Bienkowski, M., Feng, M., Means, B. (2012). Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief
•Chatti, M.A., Dyckhoff, A.L., Schroeder, U. and Thüs, H. (2012) ‘A reference model for learning analytics’, Int. J. Technology Enhanced Learning, Vol. 4, Nos. 5/6, pp.318–331.
•del Blanco, A. et al. (2013). E-Learning Standards and Learning Analytics: Can Data Collection Be Improved by Using Standard Data Models?
•DonzelliniG., Ponta D. (2007) A Simulation Environment for e-Learning in Digital Design. Trans. on Industrial Electronics, vol. 54, no. 6: 3078—3085.
•Glahn, C., Specht, M., Koper, R. (2007) Smart indicators on learning interactions. In: Duval, E., Klamma, R., Wolpers, M. (eds.) EC- TEL 2007. LNCS, vol.4753, pp. 56–70. Springer, Heidelberg.
•Gyllstrom, K. (2009). Enriching Personal Information Management with Document Interaction Histories: A Thesis
•Gyllstrom, K. (2009) Passages through time: chronicling users' information interaction history by recording when and what they read, Proceedings of the 14th international conference on Intelligent user interfaces, February 08-11, 2009, Sanibel Island, Florida, USA [doi>10.1145/1502650.1502673]
•Romero, C., Ventura, S. (2007). Educational Data Mining: A Survey from 1995 to 2005.
•Romero, C., Ventura, S. (2010) Educational Data Mining: A Review of the State-of-the-Art.
•Siemens, G., Baker, S.J.d. (2010). Learning Analytics and Educational Data Mining: Towards Communication and Collaboration