Workshop on methodology in learning analytics (MLA)

Learning analytics is an interdisciplinary and inclusive field, a fact which makes the establishment of methodological norms both challenging and important. This community-building workshop intends to convene methodology-focused researchers to discuss new and established approaches, comment on the state of current practice, author pedagogical manuscripts, and co-develop guidelines to help move the field forward with quality and rigor

A-posteriori provenance-enabled linking of publications and datasets via crowdsourcing

This paper aims to share with the digital library community different opportunities to leverage crowdsourcing for a-posteriori capturing of dataset citation graphs. We describe a practical approach, which exploits one possible crowdsourcing technique to collect these graphs from domain experts and proposes their publication as Linked Data using the W3C PROV standard. Based on our findings from a study we ran during the USEWOD 2014 workshop, we propose a semi-automatic approach that generates metadata by leveraging information extraction as an additional step to crowdsourcing, to generate high-quality data citation graphs. Furthermore, we consider the design implications on our crowdsourcing approach when non-expert participants are involved in the process<br/

A review on data fusion in multimodal learning analytics and educational data mining

The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye-tracking, user logs, and click-stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area

Location Privacy in the Era of Big Data and Machine Learning

Location data of individuals is one of the most sensitive sources of information that once revealed to ill-intended individuals or service providers, can cause severe privacy concerns. In this thesis, we aim at preserving the privacy of users in telecommunication networks against untrusted service providers as well as improving their privacy in the publication of location datasets. For improving the location privacy of users in telecommunication networks, we consider the movement of users in trajectories and investigate the threats that the query history may pose on location privacy. We develop an attack model based on the Viterbi algorithm termed as Viterbi attack, which represents a realistic privacy threat in trajectories. Next, we propose a metric called transition entropy that helps to evaluate the performance of dummy generation algorithms, followed by developing a robust dummy generation algorithm that can defend users against the Viterbi attack. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset. For privacy preserving data publishing, an enhanced framework for anonymization of spatio-temporal trajectory datasets termed the machine learning based anonymization (MLA) is proposed. The framework consists of a robust alignment technique and a machine learning approach for clustering datasets. The framework and all the proposed algorithms are applied to the Geolife dataset, which includes GPS logs of over 180 users in Beijing, China

Museum Learning via Social Media: (How) Can Interactions on Twitter Enhance the Museum Learning Experience?

Museums are rich sources of artifacts, people and potential dialogic interactions. Recent developments in web technologies pose big challenges to museums to integrate such technologies in their learning provision. The study presented here is concerned with the potential of how school visits to museums can be enhanced by the use of social media. The Museum of London (MoL) is selected as the site of the study and the participants were a Year 9 History class (13-14 years old) in a secondary school in Milton Keynes. It draws on Falk and Dierking’s (2000) Contextual Model of Learning and considers evidence of meaning making from students’ tweets and activity on-site. Observational data during the visit, the visit’s Twitter stream and post-visit interview data with the participants is presented and analysed. It is argued that use of Twitter, a microblogging platform (http://twitter.com), enhances the social interaction around museum artifacts and thus, the process of shared construction of meaning making, which can enrich the museum experience

FutureBeef Stocktake Plus app - Beyond Development: Extension and strategy

Undertaking regular land condition assessments and forage budgets is considered part of best management practice (BMP) for graziers in northern Australia. These management tasks can be complex and require a number of steps both in the paddock and in the office, along with supporting tools and skills to be able to answer the questions: “what is my current pasture condition” and “how long will this feed last given the stock in the paddock”? The Stocktake Plus mobile application (the app) has been developed as a grazing land management (GLM) decision support tool for north Australian graziers, natural resource management (NRM) groups and public and private service providers. People can use the app to improve their knowledge and understanding of land condition monitoring and forage budgeting with the focus on making better land resource management decisions. Since the app’s release in April 2013, there has been a strong extension framework based around it. Department of Agriculture and Fisheries (DAF) staff and other external deliverers have promoted the app to graziers and NRM staff as part of other contracted projects, in particular Stocktake workshops and MLA EDGEnetwork workshops (Grazing Land Management EDGE, Grazing Fundamentals and Nutrition EDGE). The app’s uptake through registrations has well exceeded the original forecast of 500 registered users. To date over 1500 users have registered and downloaded the app, and of these 61% (882) are cattle graziers, 12% (172) are mixed enterprise producers and 4% (55) are sheep producers. Independent evaluation shows that many users are positive about the app – praising it for being intuitive, easy to use and navigate, simple and straightforward, and very user friendly. For some people however, satisfaction and adoption levels were hampered by frustrations caused by technical, design, and learning issues. While overall refinements of the app and adoption were progressing well, a significant technical failure in database back-up function is now stalling further progress

EFAR-MMLA: An evaluation framework to assess and report generalizability of machine learning models in MMLA

Producción CientíficaMultimodal Learning Analytics (MMLA) researchers are progressively employing machine learning (ML) techniques to develop predictive models to improve learning and teaching practices. These predictive models are often evaluated for their generalizability using methods from the ML domain, which do not take into account MMLA’s educational nature. Furthermore, there is a lack of systematization in model evaluation in MMLA, which is also reflected in the heterogeneous reporting of the evaluation results. To overcome these issues, this paper proposes an evaluation framework to assess and report the generalizability of ML models in MMLA (EFAR-MMLA). To illustrate the usefulness of EFAR-MMLA, we present a case study with two datasets, each with audio and log data collected from a classroom during a collaborative learning session. In this case study, regression models are developed for collaboration quality and its sub-dimensions, and their generalizability is evaluated and reported. The framework helped us to systematically detect and report that the models achieved better performance when evaluated using hold-out or cross-validation but quickly degraded when evaluated across different student groups and learning contexts. The framework helps to open up a “wicked problem” in MMLA research that remains fuzzy (i.e., the generalizability of ML models), which is critical to both accumulating knowledge in the research community and demonstrating the practical relevance of these techniques.Fondo Europeo de Desarrollo Regional - Agencia Nacional de Investigación (grants TIN2017-85179-C3-2-R and TIN2014-53199-C3-2-R)Fondo Europeo de Desarrollo Regional - Junta de Castilla y León (grant VA257P18)Comisión Europea (grant 588438-EPP-1- 2017-1-EL-EPPKA2-KA