Aneesha Bakharia

A conceptual framework linking learning design with learning analytics

In this paper we present a learning analytics conceptual framework that supports enquiry-based evaluation of learning designs. The dimensions of the proposed framework emerged from a review of existing analytics tools, the analysis of interviews with teachers, and user scenarios to understand what types of analytics would be useful in evaluating a learning activity in relation to pedagogical intent. The proposed framework incorporates various types of analytics, with the teacher playing a key role in bringing context to the analysis and making decisions on the feedback provided to students as well as the scaffolding and adaptation of the learning design. The framework consists of five dimensions: temporal analytics, tool-specific analytics, cohort dynamics, comparative analytics and contingency. Specific metrics and visualisations are defined for each dimension of the conceptual framework. Finally the development of a tool that partially implements the conceptual framework is discussed.

Recipe for success: lessons learnt from using xAPI within the connected learning analytics toolkit

An ongoing challenge for Learning Analytics research has been the scalable derivation of user interaction data from multiple technologies. The complexities associated with this challenge are increasing as educators embrace an ever growing number of social and content-related technologies. The Experience API (xAPI) alongside the development of user specific record stores has been touted as a means to address this challenge, but a number of subtle considerations must be made when using xAPI in Learning Analytics. This paper provides a general overview to the complexities and challenges of using xAPI in a general systemic analytics solution - called the Connected Learning Analytics (CLA) toolkit. The importance of design is emphasised, as is the notion of common vocabularies and xAPI Recipes. Early decisions about vocabularies and structural relationships between statements can serve to either facilitate or handicap later analytics solutions. The CLA toolkit case study provides us with a way of examining both the strengths and the weaknesses of the current xAPI specification, and we conclude with a proposal for how xAPI might be improved by using JSON-LD to formalise Recipes in a machine readable form.

Interactive Topic Modeling for aiding Qualitative Content Analysis

Topic Modeling algorithms are rarely used to support the qualitative content analysis process. The main contributing factors for the lack of mainstream adoption can be attributed to the perception that Topic Modeling produces topics of poor quality and that content analysts do not trust the derived topics because they are unable to supply domain knowledge and interact with the algorithm. In this paper, interactive Topic Modeling algorithms namely Dirichlet-Forrest Latent Dirichlet Allocation and Penalised Non-negative Matrix Factorisation, are evaluated with respect to their ability to aid qualitative content analysis. More specifically, the relationship between interactivity, interpretation, topic coherence and trust in interactive content analysis is examined. The findings indicate that providing content analysts with the ability to interact with Topic Modeling algorithms produces topics that are directly related to their research questions. However, a number of improvements to these algorithms were also identified which have the potential to influence future algorithm development to better meet the requirements of qualitative content analysts.

User Innovation Networks and Research Challenges

User Innovation Networks (UIN) has been considered the open innovation model for this century as it functions entirely independently of manufacturers. This paper discusses two UIN cases, Daz3D, as well as Linux Dell and IBM cooperation as regards research challenges about the community of practice and interface used. It concludes that current technology only now started touching global and extreme collaboration for creativity and innovation.

Towards Cross-domain MOOC Forum Post Classification

Preliminary research is presented on the generalisability of confusion, urgency and sentiment classifiers for MOOC forum posts. The Stanford MOOCPosts data set is used to train classifiers with forum posts from individual courses and validate these classifiers on MOOC forum posts from other domain areas. While low cross-domain classification accuracy is achieved, the experiment highlights the need for transfer learning and domain adaptation algorithms; and provides insight into the types of algorithms required within an educational context.

The connected learning analytics toolkit

This demonstration introduces the Connected Learning Analytics (CLA) Toolkit. The CLA toolkit harvests data about student participation in specified learning activities across standard social media environments, and presents information about the nature and quality of the learning interactions.

ADCS reaches adulthood: an analysis of the conference and its community over the last eighteen years

How influential is the Australian Document Computing Symposium (ADCS)? What do ADCS articles speak about and who cites them? Who is the ADCS community and how has it evolved? This paper considers eighteen years of ADCS, investigating both the conference and its community. A content analysis of the proceedings uncovers the diversity of topics covered in ADCS and how these have changed over the years. Citation analysis reveals the impact of the papers. The number of authors and where they originate from reveal who has contributed to the conference. Finally, we generate co-author networks which reveal the collaborations within the community. These networks show how clusters of researchers form, the effect geographic location has on collaboration, and how these have evolved over time.