Wiki-LDA: A Mixed-Method Approach for Effective Interest Mining on Twitter Data

Learning analytics (LA) and Educational data mining (EDM) have emerged as promising technology-enhanced learning (TEL) research areas in recent years. Both areas deal with the development of methods that harness educational data sets to support the learning process. A key area of application for LA and EDM is learner modelling. Learner modelling enables to achieve adaptive and personalized learning environments, which are able to take into account the heterogeneous needs of learners and provide them with tailored learning experience suited for their unique needs. As learning is increasingly happening in open and distributed environments beyond the classroom and access to information in these environments is mostly interest-driven, learner interests need to constitute an important learner feature to be modeled. In this paper, we focus on the interest dimension of a learner model and present Wiki-LDA as a novel method to effectively mine user’s interests in Twitter. We apply a mixed-method approach that combines Latent Dirichlet Allocation (LDA), text mining APIs, and wikipedia categories. Wiki-LDA has proven effective at the task of interest mining and classification on Twitter data, outperforming standard LDA.

Special issue on: Learning Analytics (Editorial)

Maria Jesus Rodriguez Triana

With the general technological advances of the recent years, current learning environments amass an abundance of data. Albeit such data offer the chance of better understand the learning process, stakeholders – learners, teachers and institutions – often need additional support to make sense of it (Dyckhoff et al., 2013; Macfadyen and Dawson, 2012). The acknowledgement of these needs is at the heart of the recent emergence of Learning Analytics (LA), a research area that draws from multiple disciplines such as educational science, information and computer science, sociology, psychology, statistics and educational data mining (Buckingham Shum and Ferguson, 2012). This multidisciplinarity in LA has motivated the work done by Ferguson (2012), which provides a first review of the drivers, development and challenges of this novel and young research area. Our understanding of learning analytics is based on the definition from the Society for Learning Analytics (SoLAR – Society for Learning Analytics1) which specifies that “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”. Since 2011, the Horizon reports list Learning Analytics as a hot topic in higher education and indicate the importance of data for this field (Johnson et al., 2011). Learning analytics are able to provide a fresh view on understanding of teaching and learning by observing patterns of complex data (Johnson et al., 2012). Furthermore, it will influence the evolution of higher education in a great measure. Nowadays, learners have access to a huge amount of online information having themselves the possibility of being content creators and information sharers. Therefore the quantity of available information grows in an exponential way, once that each and every citizen can access and produce information. For these purposes, learners have at their disposal many online resources, including LMSs, VLEs, MOOCs and many other online tools that facilitate the learning process and the development of competences. Taking into account these online learning facilities and therefore the learners’ acquisition of knowledge, it is also easier to measure and analyse their experiences by using learning analytics tools. Different online courses and institutions provide dashboards with information about student experiences, flaws and successes. Although the investigation of behavioural specific data makes learning analytics complex, the time comes to utilise personalised learning environments adapted to students learning paths, skills, previous knowledge, competences and motivation.

2013

Monitoring, Awareness and Reflection in Blended Technology Enhanced Learning: a Systematic Review

Denis Gillet, Adrian Christian Holzer, Luis Pablo Prieto Santos, Maria Jesus Rodriguez Triana, Beat Adrian Schwendimann, Mina Shirvani Boroujeni, Andrii Vozniuk

Education is experiencing a paradigm shift towards blended learning models in technology-enhanced learning (TEL). Despite the potential benefits of blended learning, it also entails additional complexity in terms of monitoring, awareness and reflection, as learning happens across different spaces and modalities. In recent years, literature on Learning Analytics (LA) and Educational Data Mining (EDM) has gained momentum and started to address the issue. To provide a clear picture of the current state of the research on the topic and to outline open research gaps, this paper presents a systematic literature review of the state-of-the-art of research in LA and EDM on monitoring, awareness and reflection in blended TEL scenarios. The search included six main academic databases in TEL that were enriched with the proceedings of the workshop on ’Awareness and Reflection in TEL’ (ARTEL), resulting in 1089 papers out of which 40 papers were included in the final analysis.

2017

AngeLA: Putting the Teacher in Control of Student Privacy in the Online Classroom

Denis Gillet, Sten Govaerts, Andrii Vozniuk

Learning analytics (LA) is often considered as a means to improve learning and learning environments by measuring student behaviour, analysing the tracked data and acting upon the results. The use of LA tools implies recording and processing of student activities conducted on software platforms. This paper proposes a flexible, contextual and intuitive way to provide the teacher with full control over student activity tracking in online learning environments. We call this approach AngeLA, inspired by an angel guarding over LA privacy. AngeLA mimics in a virtual space the privacy control mechanism that works well in a physical room: if a person is present in a room, she is able to observe all activities happening in the room. AngeLA serves two main purposes: (1) it increases the awareness of teachers about the activity tracking and (2) provides an intuitive way to manage the activity tracking permissions. This approach can be applied to various learning environments and social media platforms. We have implemented AngeLA in Graasp, a social platform that fosters collaborative activities.

2014

Special issue on: Learning Analytics (Editorial)

Maria Jesus Rodriguez Triana

2013