Postgraduate Course: Learning Analytics: Process and Theory (EDUA11339)
Course Outline
| School | Moray House School of Education |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course provides a framework for understanding and critically discussing the emerging field of learning analytics. Students will learn about the distinction between learning analytics, educational data mining, and big data, and the relationship of learning analytics existing fields. Perspectives on what learning analytics should be will be connected to philosophy and theory on the nature of design and inquiry. We will consider what it means for a learning analytics analysis or model to be valid, and the key challenges to the effective and appropriate use of learning analytics. |
| Course description |
The course is structured around a number of activities. Specifically, each week will have a set of:
¿ Readings introducing the topics of learning analytics covered by the course. The topics will be adjusted each to acknowledge the rapid development of the field of learning analytics and its theory and processes.
¿ Each of these readings will be accompanied by a series of tutor-provided questions that will help scaffold participants¿ posts to asynchronous online discussion posts. The purpose of these discussions is to create a space for the participants to engage with social knowledge construction activities, negotiate the meaning of the topics studied with their peers, and get to appreciate and critical discuss different viewpoints to learning analytics.
¿ The summative assessments will be accompanied with formative feedback to inform and guide following assessments in the course. The three main assessments guide the participants through a process of the development of their ideas ¿ from early literature review to project proposal to project execution, and reporting and presentation of the findings.
¿ To increase the flexibility necessary to a globally-distributed cohort, asynchronous online activities are primarily planned. To increase access to the tutor, the course will feature weekly synchronous discussion session with the instructor and scheduled weekly online chats.
The course will follow the university's instructions for creating accessible online content. The course will be offered through Moodle and the Moodle accessibility guidelines will be followed.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2017/18, Available to all students (SV1)
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Quota: 20 |
| Course Start |
Semester 2 |
| Course Start Date |
15/01/2018 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Online Activities 40,
Formative Assessment Hours 10,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
146 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Summative
Assignments are designed to be cumulative while remaining distinct.
Assignment 1: Critical literature review paper (35%)
The goal of this assignment is to write a literature review paper (1,500) on a learning analytics topic. This assignment will help define a research problem for the project that the students will be pursuing in assignments 2 and 3.
Assignment 2: Collaborative formulation of application or research proposal (20%)
The goal of this assignment is to help students formulate their research proposals for the project in assignment 3 and discuss their research proposal with the peers. Students will provide constructive feedback to their peers about their proposed research, the quality of which will be assessed and contribute to the final mark.
A research proposal of 500 words will be written by each student, which will be shared in the course space for peer feedback. All students will be expected both to provide feedback, and to respond constructively to feedback from others. The participation in the discussions about peers¿ proposals will constitute 20% of the assessment weighting for assignment 2.
Assignment 3 ¿ Learning analytics planning paper (40%)
This assignment is the development of a detailed plan for the application or research proposed in learning analytics. The students will also conduct a pilot project based on their proposals and report on the findings. This assignment builds on the literature review from Assignment 1, and the research problem formulated and developed in Assignment 2.
Students will write a research paper of 2,500 words which will constitute 65% of the assignment weighting; they will also give an online presentation of their work which will constitute the 25% of the of the assignment weighting. Students will provide constructive feedback to their peers about their proposed research, the quality of which will be assessed and contribute 10% of the assessment weighting for assignment 3.
Formative
Formative feedback will be provided throughout the course through tutor and peer feedback on discussion posts. Peer feedback will also be provided on the assignments, including the final presentation.
Students will also write weekly reflections on the course readings and critically evaluate each other¿s reflections, which will constitute the final 5% of the course grade. Consistent with the literature in online education and with practice elsewhere in the MSc Digital Education, the purpose of this is to receive formative feedback from both peers and the instructor and encourage social knowledge construction activities that will contribute to the three assessments in the course.
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| Feedback |
Extensive tutor and peer feedback will be provided as described above. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Describe and critically analyse learning analytics process and theory
- Review, integrate and critically assess emerging trends in learning analytics literature
- Develop a proposal for a piece of research or application using learning analytics in an educational setting, based in a critical understanding of the literature
- Develop a detailed plan for the learning analytics application or research proposed, and critically assess its main elements
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Reading List
Anderson, J.R., Reder, L.M., Simon, H.A. (1996) Situated Learning and Education. Educational Researcher, 25 (4), 5-11.
Anderson, J.R., Reder, L.M., Simon, H.A. (1997) Situative Versus Cognitive Perspectives: Form Versus Substance. Educational Researcher, 26 (1), 18-21.
Arnold, K.E. (2010). Signals: Applying academic analytics. Educause Quarterly, 33, 1-10.
Baker, R.S.J.d. (2010) Mining Data for Student Models. In Nkmabou, R., Mizoguchi, R., & Bourdeau, J. (Eds.) Advances in Intelligent Tutoring Systems, pp. 323-338. Secaucus, NJ: Springer.
Baker, R.S.J.d., Yacef, K. (2009) The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1 (1), 3-17.
Broderick, Z., O'Connor, C., Mulcahy, C., Heffernan, N. & Heffernan, C. (2011). Increasing Parent Engagement in Student Learning Using an Intelligent Tutoring System. Journal of Interactive Learning Research, 22 (4), 523-550.
Colvin, C., Rogers, T., Wade, A., Dawson, S., Ga¿evi¿, D., Buckingham Shum, S., Nelson, K., Alexander, S., Lockyer, L., Kennedy, G., Corrin, L., Fisher, J. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Canberra, ACT, Australia: Australian Government's Office for Learning and Teaching.
Ferguson, R. (2012) Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning (IJTEL), 4 (5/6), 304-317.
Ga¿evi¿, D., Dawson, S., & Siemens, G. (2015). Let¿s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.
Greeno, J.G. (1997) On Claims That Answer the Wrong Question. Educational Researcher, 26(1), 5-17.
Hand, D.J., Blunt, G., Kelly, M.G., Adams, N.M. (2000) Data Mining for Fun and Profit. Statistical Science, 15 (2), 111-126.
Hershkovitz, A., de Baker, R. S. J., Gobert, J., Wixon, M., & Sao Pedro, M. (2013). Discovery With Models A Case Study on Carelessness in Computer-Based Science Inquiry. American Behavioral Scientist, 57(10), 1480-1499.
Macfadyen, L. P., Dawson, S., Pardo, A., & Gasevic, D. (2014). Embracing big data in complex educational systems: The learning analytics imperative and the policy challenge. Research & Practice in Assessment, 9(2), 17-28.
Romero, C., Ventura, S. (2007) A Survey from 1995 to 2005. Expert Systems with Applications,33 (1), 135-146.
Rupp, A.A., Gushta, M., Mislevy, R.J., Shaffer, D.W. (2010) Evidence-Centered Design of Epistemic Games: Measurement Principles for Complex Learning Environments. The Journal of Technology, Learning, and Assessment, 8 (4), 4-47.
Scheuer, O., & McLaren, B. M. (2012). Educational data mining. In N. Seel (Ed.) Encyclopedia of the Sciences of Learning (pp. 1075-1079). Springer US.
Shute, V.J., Ventura, M., Bauer, M., Zapata-Rivera, D. (2009) Melding the Power of Serious Games and Embedded Asssessment to Monitor and Foster Learning. In U. Ritterfeld, M. Cody, & P. Vorderer (Eds.), Serious Games: Mechanisms and Effects, 295-321.
Siemens, G. (2013) Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57 (10), 1380-1400.
Siemens, G., & d Baker, R. S. (2012, April). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.
Simon, H. A. (1996). The sciences of the artificial (Vol. 136). MIT press.
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Additional Information
| Graduate Attributes and Skills |
A. Research and Enquiry
To be able to identify, define and analyse conceptual and/ or practical problems in learning analytics through the critical appraisal of existing evidence.
To be able to generative methodologically rigorous, ethics-based, and innovative solutions appropriate to the broader context of learning analytics.
B. Personal and Intellectual Autonomy
To be able to exercise substantial autonomy and initiative in the identification and execution of their intended learning activities.
To be independent learners able to develop and maintain a critical approach to issues in learning analytics.
C. Communication
To be make effective use of the multimodal capabilities of digital technologies to communicate appropriate knowledge and understanding of emerging concepts and practices in learning analytics.
D. Personal Effectiveness
To be able to recognise and respond to new opportunities for learning and development informed by learning analytics.
To be able to work effectively with others on different issues in learning analytics.
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| Keywords | Learning analytics,educational theory,educational data mining,big data |
Contacts
| Course organiser | Prof Dragan Gasevic
Tel: (0131 6)51 6243
Email: |
Course secretary | Ms Angela Hunter
Tel: (0131 6)51 1196
Email: |
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