19 October 2014
Data, Analytics, and Learning MOOC goes live
The long-awaited edX MOOC on Data, Analytics, and Learning went live this week. The #DALMOOC, which is taught by George Siemens, Carolyn Rosé, Dragan Gasevic, and Ryan Baker, provides an introduction to learning analytics, its tools and methods, and various ways in which it might be deployed in educational environments. It is also an experiment in its own right, allowing for multiple learning pathways: either in a standard edX xMOOC format, or as a social competency-based and self-directed cMOOC.
I have yet to engage much in the course but, at first glance, I have one small (or large, depending on how you look at it) criticism: The DALMOOC course agreement is confusing.
Data from participation in this Massive Open Online Course (MOOC) will be used for research purposes in order to gain knowledge for better design of support for student learning in MOOCs. When participants are logged in to this course, the information they enter into the course interface will be logged for analysis. The data will not be shared beyond the researchers who have approval to use this data. Personal identifiers will be replaced by unique identifiers. A possible risk is a breach of confidentiality. Participation is voluntary, and participants may stop participating at any time. There will be no cost to participants for participation in this study, and likewise no financial compensation will be offered. There may be no personal benefit from participation in the study beyond the knowledge received in the area of learning analytics, which is the topic of the course.
On the one hand, the course agreement (note: NOT a research participation agreement) is the first page that the student encounters when clicking the ‘Courseware’ tab (following registration), and implies that participation in the course is contingent upon one’s agreement to participate in a the research project. This implied contingency would seem to contradict the first ‘O’ in MOOC. On the other hand, it states that participation is voluntary and that it may be withdrawn at any time. What is not clear, is whether withdrawal from participation means withdrawal from the study or from the course. The way that this agreement is structured strongly implies that course participation requires participation in the study. As a test, I have not clicked the “I have read the above an consent to participation” button and have, to date, not been limited in my ability to participate in the course. I wonder about the ethics of this approach to gaining consent and, at the very least, wish that the language of the DALMOOC Course Agreement was less equivocal. [Read more]
21 October 2014
Study will Teach Algebra with Student-Authored Stories that Draw on Their Own Interests
A new study by Candace Walkington (Southern Methodist University) will test the effectiveness of teaching algebra by embedding algebraic concepts into students’ day-to-day lives. The study uses a mixed methodology, employing qualitative and data mining to test the effectiveness of personalized instruction on conceptual comprehension and retention, and attitudes toward math.
This is an approach that is often employed (or rather SHOULD often be employed) in the humanities (nothing like using love and sex to make sense out of Hegel’s master-slave dialectic), and resonates with the educational philosophy of John Dewey, for whom learning is a function of a concept’s importance, which, in turn, is a function of past experience, present necessity, and future aspiration. It is also an approach that might also serve to ‘catch’ more humanistically oriented students who do not consider themselves very ‘math’ or ‘science.’ [Read more]
Social Learning, Blending xMOOCs & cMOOCs, and Dual Layer MOOCs by Matt Crosslin
A really nice discussion of the design methodology for #DALMOOC. Specifically, Crosslin addresses three primary quetions, of which only two are really interesting (the third involved color selection):
- Don’t most MOOCs blend elements of xMOOCs and cMOOCs together? The xMOOC/cMOOC distinction is too simple and DALMOOC is not really doing anything different.
- Isn’t it ironic to have a Google Hangout to discuss an interactive social learning course but not allow questions or interaction?
Learning analytics using business intelligence systems by Niall Sclater
A review of several generic Business Intelligence solutions (including Cognos, Qlikview, and Tableau) which are typically employed for the sake of gaining operational insight, and ways in which they might be leveraged to gain insight into student learning experience as well.
Use of an Early Warning System by Stephen J. Aguilar
Video of a lightening talk version (~5 min) of a talk originally delivered at the 2014 Learning Analytics and Knowledge Conference, on “Perception and Use of an Early Warning System During a Higher Education Transition Program.”
Teaching and Learning in an Evolving Educational Environment by Charles Dziuban
Full video of the inaugural lecture in Emory University’s 2014-2015 Learning Analytics Speaker Series. Dziuban uses a variety of metaphors (including the Anna Karenina principle) to offer a perspective on learning analytics through the lens of the scholarship of teaching and learning, and explains the successful support model that he has implemented with faculty at the University of Central Florida.
On the Question of Validity in Learning Analytics by Adam Cooper
Cooper calls for a rethinking of the term ‘validity’ within the context of learning analytics. Although he covers himself by saying that “This post is a personal view, incomplete and lacking academic rigour,” what he nevertheless seems to call for is a conflation of methodological and ethical concerns, and a loosening of conceptual clarity in the name of facilitating practice by non-experts.
At the end of his post, Cooper asks He asks: “what do you think?” When dealing with technologies with the likelihood of significantly affecting human behavior, conceptual sophistication in both ethical and methodological matters is more, not less, important. In the absence of rigor, we run the risk of under-appreciating complexity, and implementing interventions that cause harm. What non-expert practitioners need is not a ‘dumbed-down’ vocabulary (or technology that does the work), but rather a set of expert advisors capable of fully assessing problems and solutions from a wide variety of perspectives in order to arrive at solutions that, even if not perfect, are at least fully informed.
Learning Analytics as a Metacognitive Tool
Eva Durall & Begoña Gros
The use of learning analytics is entering in the field of research in education as a promising way to support learning. However, in many cases data are not transparent for the learner. In this regard, Educational institutions shouldn’t escape the need of making transparent for the learners how their personal data is being tracked and used in order to build inferences, as well as how its use is going to affect in their learning. In this contribution, we sustain that learning analytics offers opportunities to the students to reflect about learning and develop metacognitive skills. Student-centered analytics are highlighted as a useful approach for reframing learning analytics as a tool for supporting self-directed and self-regulated learning. The article also provides insights about the design of learning analytics and examples of experiences that challenge traditional implementations of learning analytics.
Premise of Learning Analytics for Educational Context: Through Concept to Practice
Yasemin Gülbahar & Hale Ilgaz
The idea of using recorded data for evaluating the effectiveness of teaching-learning process and using the outcomes for improvement and enhancing quality lead to the emergence of the field known as “learning analytics”. Based on the analysis of this data, possible predictions could be reached to make suggestions and give decisions in order to implement interventions for the improvement of the quality of the process. Hence, the concept of “learning analytics” is a promising and important field of study, with its processes and potential to advance e-learning. In this study, learning analytics are defined in two ways – business and e-learning environments. As an e-learning environment, Moodle LMS was chosen and analyzed through SAS (Statistical Analysis System) Level of Analytics. According to the analysis, some practical ideas developed. However learning analytics seem to be mostly based on quantitative data, whereas qualitative insights can also be gained through various approaches which can be used to strengthen the numerical data by providing detailed facts about a phenomenon. Thus, in addition to focusing on the learner, for research studies at the course, program, and institutional level; the research should include instructors and administrators in order to reveal the best practices of instructional design and fulfill the premise of effective teaching.
Calls for Papers / Participation
NEW! Open Learning Analytics Network – Summit Europe Amsterdam | 1 January 2015 (APPLICATION DEADLINE: None, but spaces are limited)
Third International Conference on Data Mining & Knowledge Management Process Dubai, UAE | 23-24 January, 2015 (APPLICATION DEADLINE: 31 October 2014)
Learning at Scale 2015 Vancouver, BC (Canada) | 14 – 15 March 2015 (SUBMISSION DEADLINE: 22 October 2014)
2015 Southeast Educational Data Symposium (SEEDS) Emory University (Atlanta, GA) | 20 Feb 2015 (APPLICATION DEADLINE: 14 November 2014)
11th International Conference on Computer Supported Collaborative
Learning: “Exploring the material conditions of learning: Opportunities and
challenges for CSCL” University of Gothenburg, Sweden | 7 – 11 June 2015 (SUBMISSION DEADLINE: 17 November 2014)
28th annual Florida AI Research Symposium (FLAIRS-28) on Intelligent Learning Technologies Hollywood, Florida, USA (SUBMISSION DEADLINE: 17 November 2014)
Journals / Book Chapters
NEW! Universities and Knowledge Society Journal (RUSC): Special Section on Learning Analytics (SUBMISSION DEADLINE: 20 January 2015)
Simon Fraser University (Victoria, BC, Canada)
Tenure Track Position In Educational Technology And Learning Design – The Faculty of Education, Simon Fraser University (http://www.sfu.ca/education.html) seeks applications for a tenure-track position in Educational Technology and Learning Design at the Assistant Professor rank beginning September 1, 2015, or earlier. The successful candidate will join an existing complement of faculty engaged in Educational Technology and Learning Design, and will contribute to teaching and graduate student supervision in our vibrant Masters program at our Surrey campus and PhD program at our Burnaby campus. DEADLINE FOR APPLICATION: December 1, 2014
University of Technology, Sydney (Sydney, AUS)
Research Fellow: Data Scientist – We invite applications from highly motivated data scientists wishing to work in a dynamic team, creating tools to provide insight into diverse datasets within the university and beyond. We welcome applicants from diverse backgrounds, although knowledge of educational theory and practice will be highly advantageous. You are a great communicator, bringing expertise in some combination of statistics, data mining, machine learning and visualisation, and a readiness to stretch yourself to new challenges. We are ready to consider academic experience from Masters level to several years’ Post-Doctoral research, as well as candidates who have pursued non-academic, more business-focused tracks. DEADLINE FOR APPLICATION: Posted Until Filled
University of Michigan (Ann Arbor, MI)
Senior Digital Media Specialist – The University of Michigan is seeking a qualified Senior Digital Media Specialist to create digital content in support of online and residential educational experiences for the Office of Digital Education & Innovation (DEI). DEADLINE FOR APPLICATION: Posted Until Filled
NYU Steinhardt School of Culture, Education,and Human Developments Center for Research on Higher Education Outcomes (USA)
12-month postdoctoral position – available for a qualified and creative individual with interests in postsecondary assessment, learning analytics, data management, and institutional research.The Postdoctoral Fellow will be responsible for promoting the use of institutional data sources and data systems for the purpose of developing institutional assessment tools that can inform decision making and contribute to institutional improvement across New York University (NYU). DEADLINE FOR APPLICATION: Open Until Filled