Open University Learning Analytics dataset

Learning Analytics focuses on the collection and analysis of learners’ data to improve their learning experience by providing informed guidance and to optimise learning materials. To support the research in this area we have developed a dataset, containing data from courses presented at the Open University (OU). What makes the dataset unique is the fact that it contains demographic data together with aggregated clickstream data of students’ interactions in the Virtual Learning Environment (VLE). This enables the analysis of student behaviour, represented by their actions. The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries). The dataset is freely available at https://analyse.kmi.open.ac.uk/open_dataset under a CC-BY 4.0 license

Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence.

ABSTRACT This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Learning Analytics (LA) and Educational Data Mining (EDM) and its impact on adaptive learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of LA/EDM in generic educational strategic planning. We examined the literature on experimental case studies conducted in the domain during the past six years (2008)(2009)(2010)(2011)(2012)(2013). Search terms identified 209 mature pieces of research work, but inclusion criteria limited the key studies to 40. We analyzed the research questions, methodology and findings of these published papers and categorized them accordingly. We used non-statistical methods to evaluate and interpret findings of the collected studies. The results have highlighted four distinct major directions of the LA/EDM empirical research. We discuss on the emerged added value of LA/EDM research and highlight the significance of further implications. Finally, we set our thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations

Towards an educational data literacy framework: enhancing the profiles of instructional designers and e-tutors of online and blended courses with new competences

In the era of digitalization of learning and teaching processes, Educational Data Literacy (EDL) is highly valued and is becoming essential. EDL is conceptualized as the ability to collect, manage, analyse, comprehend, interpret, and act upon educational data in an ethical, meaningful, and critical manner. The professionals in the field of digitally supported education, i.e., Instructional Designers (IDs) and e-Tutors (eTUTs) of online and blended courses, need to be ready to inform their decisions with educational data, and face the upcoming data-related challenges; they need to update and enhance their profiles with relevant competences. This paper proposes a framework for EDL competence profiles of IDs/eTUTs and evaluates the proposal with the participation of worldwide professionals (N = 210) with experience in digitally supported education. The evaluation aims at validating the proposal and assesses (a) the current EDL-readiness of IDs/eTUTs; and (b) the extent to which the framework captures and describes the essential EDL competences. The findings indicate that professionals are not EDL-competent yet, but the proposed dimensions and related competences are offering a solid approach to support EDL development

Data-Driven Analysis of Engagement in Gamified Learning Environments: A Methodology for Real-Time Measurement of MOOCs

Welfare and economic development is directly dependent on the availability of highly skilled and educated individuals in society. In the UK, higher education is accessed by a large percentage of high school graduates (50% in 2017). Still, in Brazil, a limited number of pupils leaving high schools continue their education (up to 20%). Initial pioneering efforts of universities and companies to support pupils from underprivileged backgrounds, to be able to succeed in being accepted by universities include personalised learning solutions. However, initial findings show that typical distance learning problems occur with the pupil population: isolation, demotivation, and lack of engagement. Thus, researchers and companies proposed gamification. However, gamification design is traditionally exclusively based on theory-driven approaches and usually ignore the data itself. This paper takes a different approach, presenting a large-scale study that analysed, statistically and via machine learning (deep and shallow), the first batch of students trained with a Brazilian gamified intelligent learning software (called CamaleOn), to establish, via a grassroots method based on learning analytics, how gamification elements impact on student engagement. The exercise results in a novel proposal for real-time measurement on Massive Open Online Courses (MOOCs), potentially leading to iterative improvements of student support. It also specifically analyses the engagement patterns of an underserved community

The use of tools of data mining to decision making in engineering education—A systematic mapping study

In recent years, there has been an increasing amount of theoretical and applied research that has focused on educational data mining. The learning analytics is a discipline that uses techniques, methods, and algorithms that allow the user to discover and extract patterns in stored educational data, with the purpose of improving the teaching‐learning process. However, there are many requirements related to the use of new technologies in teaching‐learning processes that are practically unaddressed from the learning analytics. In an analysis of the literature, the existence of a systematic revision of the application of learning analytics in the field of engineering education is not evident. The study described in this article provides researchers with an overview of the progress made to date and identifies areas in which research is missing. To this end, a systematic mapping study has been carried out, oriented toward the classification of publications that focus on the type of research and the type of contribution. The results show a trend toward case study research that is mainly directed at software and computer science engineering. Furthermore, trends in the application of learning analytics are highlighted in the topics, such as student retention or dropout prediction, analysis of academic student data, student learning assessment and student behavior analysis. Although this systematic mapping study has focused on the application of learning analytics in engineering education, some of the results can also be applied to other educational areas

AI-Driven Assessment of Students: Current Uses and Research Trends

During the last decade, the use of AIs is being incorporated into the educational field whether to support the analysis of human behavior in teachinglearning contexts, as didactic resource combined with other technologies or as a tool for the assessment of the students. This proposal presents a Systematic Literature Review and mapping study on the use of AIs for the assessment of students that aims to provide a general overview of the state of the art and identify the current areas of research by answering 6 research questions related with the evolution of the field, and the geographic and thematic distribution of the studies. As a result of the selection process this study identified 20 papers focused on the research topic in the repositories SCOPUS and Web of Science from an initial amount of 129. The analysis of the papers allowed the identification of three main thematic categories: assessment of student behaviors, assessment of student sentiments and assessment of student achievement as well as several gaps in the literature and future research lines addressed in the discussion

Opportunities and Challenges in Using Learning Analytics in Learning Design

Educational institutions are designing, creating and evaluating courses to optimize learning outcomes for highly diverse student populations. Yet, most of the delivery is still monitored retrospectively with summative evaluation forms. Therefore, improvements to the course design are only implemented at the very end of a course, thus missing to benefit the current cohort. Teachers find it difficult to interpret and plan interventions just-in-time. In this context, Learning Analytics (LA) data streams gathered from ‘authentic’ student learning activities, may provide new opportunities to receive valuable information on the students' learning behaviors and could be utilised to adjust the learning design already "on the fly" during runtime. We presume that Learning Analytics applied within Learning Design (LD) and presented in a learning dashboard provide opportunities that can lead to more personalized learning experiences, if implemented thoughtfully. In this paper, we describe opportunities and challenges for using LA in LD. We identify three key opportunities for using LA in LD: (O1) using on demand indicators for evidence based decisions on learning design; (O2) intervening during the run-time of a course; and, (O3) increasing student learning outcomes and satisfaction. In order to benefit from these opportunities, several challenges have to be overcome. We mapped the identified opportunities and challenges in a conceptual model that considers the interaction of LA in LD.SURF Foundation & NRO under the REFLECTOR project grant