OpenU contributions in Leuven at LAK13 next month…
Update: all presentations now online
Buckingham Shum, S., Baker, R., Behrens, J., Hawksey, M., Jeffery, N. and Pea, R. (2013). Educational Data Scientists: A Scarce Breed (Panel). Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://simon.buckinghamshum.net/2013/03/lak13-edu-data-scientists-scarce-breed
The Educational Data Scientist is currently a poorly understood, rarely sighted breed. Reports vary: some are known to be largely nocturnal, solitary creatures, while others have been reported to display highly social behaviour in broad daylight. What are their primary habits? How do they see the world? What ecological niches do they occupy now, and will predicted seismic shifts transform the landscape in their favour? What survival skills do they need when running into other breeds? Will their numbers grow, and how might they evolve? In this panel, the conference will hear and debate not only broad perspectives on the terrain, but will have been exposed to some real life specimens, and caught glimpses of the future ecosystem. [Post your questions to the Panellists…]
Buckingham Shum, S., De Laat, M., De Liddo, A., Ferguson, R., Kirschner, P., Ravencroft, A., Sándor, Á. and Whitelock, D. (2013). 1st International Workshop on Discourse-Centric Learning Analytics. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8 April 2013, Leuven, Belgium. http://www.solaresearch.org/events/lak/lak13/dcla13
Written discourse is a major class of data that learners produce in online environments, arguably the primary class of data that can give us insights into deeper learning and higher order qualities such as critical thinking, argumentation, mastery of complex ideas, empathy, collaboration and interpersonal skills. It is central to the collaborative and social learning that takes place online and there is a correspondingly significant literature on discourse analysis for online learning/CSCL. Computational linguistics research has developed a rich array of automated tools for machine interpretation of human discourse, but work to develop these tools in the context of learning is at a relatively early stage. Moreover, there is a significant difference between the use of such tools to assist researchers in discourse analysis, and their deployment on platforms in order to provide meaningful analytics for learners and educators. A major class of learning analytic will emerge at the intersection of research into learning dynamics, deliberation platforms, and computational linguistics. What will make these learning analytics, as opposed to research that sits in any of the above categories, will be their use to generate information displays that help learners and/or educators to understand where significant discourse patterns are happening and that support interventions to improve discourse for learning.
Clow, D. (2013). MOOCs and the Funnel of Participation. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36657
Massive Online Open Courses (MOOCs) are growing substantially in numbers, and also in interest from the educational community. MOOCs offer particular challenges for what is becoming accepted as mainstream practice in learning analytics.
Partly for this reason, and partly because of the relative newness of MOOCs as a widespread phenomenon, there is not yet a substantial body of literature on the learning analytics of MOOCs. However, one clear finding is that drop-out/non-completion rates are substantially higher than in more traditional education.
This paper explores these issues, and introduces the metaphor of a ‘funnel of participation’ to reconceptualise the steep drop-off in activity, and the pattern of steeply unequal participation, which appear to be characteristic of MOOCs and similar learning environments. Empirical data to support this funnel of participation are presented from three online learning sites: iSpot (observations of nature), Cloudworks (‘a place to share, find and discuss learning and teaching ideas and experiences’), and openED 2.0, a MOOC on business and management that ran between 2010-2012. Implications of the funnel for MOOCs, formal education, and learning analytics practice are discussed.
d’Aquin, M. and Jay, N. (2013). Interpreting Data Mining Results with Linked Data for Learning Analytics: Motivation, Case Study and Direction. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36660
Learning analytics by nature relies on computational information processing activities intended to extract from raw data some interesting aspects that can be used to obtain insights into the behaviours of learners, the design of learning experiences, etc. There is a large variety of computational techniques that can be employed, all with interesting properties, but it is the interpretation of their results that really forms the core of the analytics process. In this paper, we look at a specific data mining method, namely sequential pattern extraction, and we demonstrate an approach that exploits available linked open data for this interpretation task. Indeed, we show through a case study relying on data about students’ enrolment in course modules how linked data can be used to provide a variety of additional dimensions through which the results of the data mining method can be explored, providing, at interpretation time, new input into the analytics process.
d’Aquin, M., Dietze, S., Drachsler, H. and Herder, E. (2013). Tutorial: Using Linked Data in Learning Analytics. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium. http://linkedup-project.eu/2013/03/17/using-linked-data-in-learning-analytics-a-tutorial-by-the-linkedup-consortium
Linked Data is a set of principles and technologies aimed at using the architecture of the web to share, expose and integrate data in a global, collaborative space. This tutorial intends to provide Learning Analytics practitioners with the basic knowledge and skills required to exploit the new possibilities offered by linked data, especially through exploring the wealth of data sources already available in the linked data cloud. We will therefore introduce the basic technologies and practices generally associated with Linked Data, including graph-based data modelling with RDF and relevant vocabularies, data discovery on the linked data cloud and the use of linked data endpoints (with SPARQL). Since the focus of the tutorial is on the concrete use of these technologies and practices within a Learning Analytics scenario, a large part of the sessions will be dedicated to hands-on exercises with data and use cases of relevance to Learning Analytics.
In addition, the tutorial will be used as a channel to present initial outcomes of the LinkedUp project, like the LinkedUp data pool and the LinkedUp Evaluation Framework. Participants to the tutorial will be encouraged to push further their ideas regarding the possible applications of Linked Data in Learning analytics scenarios through collaborating with members of LinkedUp and participating to the LinkedUp Challenge: the application development competition organized by the project. These particular activities will be concretely materialized through the inclusion as key sessions in the tutorial of activities around the LinkedUp‐supported “LAK Data Challenge”, as well as interactive brainstorming sessions around possible use cases for linked data in Learning Analytics scenarios, and their possible realisation.
d’Aquin, M., Dietze, S., Drachsler, H., Herder, E. and Taibi, D. (2013). The LAK Data Challenge. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium. http://www.solaresearch.org/events/lak/lak-data-challenge
What do analytics on learning analytics tell us? How can we make sense of this emerging field’s historical roots, current state, and future trends, based on how its members report and debate their research? The LAK Dataset provides access to structured metadata from research publications in the field of learning analytics. Challenge submissions should exploit the LAK Dataset for a meaningful purpose. This may include submissions which cover one or more of the following, non-exclusive list of topics:
- Analysis & assessment of the emerging LAK community in terms of topics, people, citations or connections with other fields
- Innovative applications to explore, navigate and visualise the dataset (and/or its correlation with other datasets)
- Usage of the dataset as part of recommender systems
Ferguson, R., Wei, Z., He, Y. and Buckingham Shum, S. (2013). An Evaluation of Learning Analytics to Identify Exploratory Dialogue in Online Discussions. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36664
Social learning analytics are concerned with the process of knowledge construction as learners build knowledge together in their social and cultural environments. One of the most important tools employed during this process is language. In this paper we take exploratory dialogue, a joint form of co-reasoning, to be an external indicator that learning is taking place. Using techniques developed within the field of computational linguistics, we build on previous work using cue phrases to identify exploratory dialogue within online discussion. Automatic detection of this type of dialogue is framed as a binary classification task that labels each contribution to an online discussion as exploratory or non-exploratory. We describe the development of a self-training framework that employs discourse features and topical features for classification by integrating both cue-phrase matching and k-nearest neighbour classification. Experiments with a corpus constructed from the archive of a two-day online conference show that our proposed framework outperforms other approaches. A classifier developed using the self-training framework is able to make useful distinctions between the learning dialogue taking place at different times within an online conference as well as between the contributions of individual participants.
Knight, S., Buckingham Shum, S. and Littleton, K. (2013). Epistemology, Pedagogy, Assessment and Learning Analytics. In: Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36635
There is a well-established literature examining the relationships between epistemology (the nature of knowledge), pedagogy (the nature of learning and teaching), and assessment. Learning Analytics (LA) is a new assessment technology and should engage with this literature since it has implications for when and why different LA tools might be deployed. This paper discusses these issues, relating them to an example construct, epistemic beliefs – beliefs about the nature of knowledge – for which analytics grounded in pragmatic, sociocultural theory might be well placed to explore. This example is particularly interesting given the role of epistemic beliefs in the everyday knowledge judgements students make in their information processing. Traditional psychological approaches to measuring epistemic beliefs have parallels with high stakes testing regimes; this paper outlines an alternative LA for epistemic beliefs which might be readily applied to other areas of interest. Such sociocultural approaches afford opportunity for engaging LA directly in high quality pedagogy.
Knight, S. and Littleton, K. (2013). Discourse, Computation and Context – Sociocultural DCLA Revisited. 1st International Workshop on Discourse-Centric Learning Analytics. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8 April 2013, Leuven, Belgium. http://oro.open.ac.uk/36640
This paper expands the sociocultural analysis of earlier discourse centric learning analytics (DCLA) to discuss the pedagogic functions of discourse, and the implications of these functions for DCLA. Given the importance of discourse for learning , and the potential of computers to (a) scaffold effective discourse and (b) give meaningful feedback on such discourse, it is important that DCLA are well theorised. Sociocultural theory emphasises context, and discourse “in action” in its analysis. If DCLA wishes to ground itself in such theory, work will need to be done to address these aspects of discourse in computational analysis. Given the potential of DCLA to provide support for educational talk – an important aspect of learning – research should be conducted to further develop DCLA approaches to such talk.
Prinsloo, P. and Slade, S. (2013). An Evaluation of Policy Frameworks for Addressing Ethical Considerations in Learning Analytics. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36934
Higher education institutions have collected and analysed student data for years, although the purpose has largely been focused on reporting and management needs. A multitude of institutional policies exist which set out in broad terms the purposes for which data will be used and how data sets will be protected. The growing advent of learning analytics as a powerful means to utilise student data to improve both learning and throughput has seen the uses to which student data is put expanding rapidly. It is fair to say though that the policies which set out institutional use of student data have not kept pace with this change.
Learning analytics can offer real-time insights into individual students‟ trajectories which can significantly impact on their learning experiences and chances of success. Institutional policy frameworks should provide not only an enabling environment for the optimal and ethical harvesting and use of data, but also clarify who benefits and under what conditions, establish conditions for consent and the de-identification of data, and address issues of vulnerability and harm. A directed content analysis of the policy frameworks of two large distance education institutions shows that current policy frameworks do not facilitate the provision of an enabling environment for learning analytics to fulfil its promise.
Schreurs, B., Teplovs, C., Ferguson, R., De Laat, M. and Buckingham Shum, S. (2013). Visualizing Social Learning Ties by Type and Topic: Rationale and Concept Demonstrator. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36891
Social Learning Analytics (SLA) are designed to support students learning through social networks, and reflective practitioners engage in informal learning through a community of practice. This short paper reports work in progress to develop SLA motivated specifically by Networked Learning Theory, drawing on the related concepts and tools of Social Network Analytics and Social Capital Theory, which provide complementary perspectives onto the structure and content of such networks. We propose that SLA based on these perspectives needs to devise models and visualizations capable of showing not only the usual SNA metrics, but the types of social tie forged between actors, and topic-specific subnetworks. We describe a technical implementation demonstrating this approach, which extends the Network Awareness Tool by automatically populating it with data from a social learning platform SocialLearn. The result is the ability to visualize relationships between people who interact around the same topics.
Simsek, D., Buckingham Shum, S., Sándor, Á., De Liddo, A. and Ferguson, R. (2013). XIP Dashboard: Visual Analytics from Automated Rhetorical Parsing of Scientific Metadiscourse. 1st International Workshop on Discourse-Centric Learning Analytics. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8 April 2013, Leuven, Belgium. http://oro.open.ac.uk/37391
A key competency that we seek to build in learners is a critical mind, i.e. ability to engage with the ideas in the literature, and to identify when significant claims are being made in articles. The ability to decode such moves in texts is essential, as is the ability to make such moves in one’s own writing. Computational techniques for extracting them are becoming available, using Natural Language (NLP) processing tuned to recognize the rhetorical signals that authors use when making a significant scholarly move. After reviewing related NLP work, we introduce the Xerox Incremental Parser (XIP), note previous work to render its output, and then motivate the design of the XIP Dashboard, a set of visual analytics modules built on XIP output, using the LAK/EDM open dataset as a test corpus. We report preliminary user reactions to a paper prototype of such a novel dashboard, and describe the visualizations implemented to date. We conclude with a summary of potential design refinements, learning platform integrations, and user evaluations.
Van Labeke, N. Whitelock, D., Field, D., Pulman, S. and Richardson, J. (2013). OpenEssayist: Extractive Summarisation & Formative Assessment of Free-Text Essays. 1st International Workshop on Discourse-Centric Learning Analytics. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8 April 2013, Leuven, Belgium. http://www.solaresearch.org/events/lak/lak13/dcla13
OpenEssayist is a system which is currently under development. It aims to provide an effective automated interactive feedback system that yields an acceptable level of support for University students writing summative essays. The Natural Language processing techniques currently employed are keyword and key phrase extraction. OpenEssayist will be piloted in September 2013 with OUUK students following a Master’s course of study.
Wolff, A. Zdrahal, Z., Nikolov, A. and Pantucek, M. (2013). Improving Retention: Predicting At-Risk Students by Analysing Clicking Behaviour in a Virtual Learning Environment. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36936
One of the key interests for learning analytics is how it can be used to improve retention. This paper focuses on work conducted at the Open University (OU) into predicting students who are at risk of failing their module. The Open University is one of the worlds largest distance learning institutions. Since tutors do not interact face to face with students, it can be difficult for tutors to identify and respond to students who are struggling in time to try to resolve the difficulty. Predictive models have been developed and tested using historic Virtual Learning Environment (VLE) activity data combined with other data sources, for three OU modules. This has revealed that it is possible to predict student failure by looking for changes in user’s activity in the VLE, when compared against their own previous behaviour, or that of students who can be categorised as having similar learning behaviour. More focused analysis of these modules applying the GUHA (General Unary Hypothesis Automaton) method of data analysis has also yielded some early promising results for creating accurate hypothesis about students who fail.