Mixing and Matching Learning Design and Learning Analytics

In the last five years, learning analytics has proved its potential in predicting academic performance based on trace data of learning activities. However, the role of pedagogical context in learning analytics has not been fully understood. To date, it has been difficult to quantify learning in a way that can be measured and compared. By coding the design of e-learning courses, this study demonstrates how learning design is being implemented on a large scale at the Open University UK, and how learning analytics could support as well as benefit from learning design. Building on our previous work, our analysis was conducted longitudinally on 23 undergraduate distance learning modules and their 40,083 students. The innovative aspect of this study is the availability of fine-grained learning design data at individual task level, which allows us to consider the connections between learning activities, and the media used to produce the activities. Using a combination of visualizations and social network analysis, our findings revealed a diversity in how learning activities were designed within and between disciplines as well as individual learning activities. By reflecting on the learning design in an explicit manner, educators are empowered to compare and contrast their design using their own institutional data

Obvious: a meta-toolkit to encapsulate information visualization toolkits. One toolkit to bind them all

This article describes “Obvious”: a meta-toolkit that abstracts and encapsulates information visualization toolkits implemented in the Java language. It intends to unify their use and postpone the choice of which concrete toolkit(s) to use later-on in the development of visual analytics applications. We also report on the lessons we have learned when wrapping popular toolkits with Obvious, namely Prefuse, the InfoVis Toolkit, partly Improvise, JUNG and other data management libraries. We show several examples on the uses of Obvious, how the different toolkits can be combined, for instance sharing their data models. We also show how Weka and RapidMiner, two popular machine-learning toolkits, have been wrapped with Obvious and can be used directly with all the other wrapped toolkits. We expect Obvious to start a co-evolution process: Obvious is meant to evolve when more components of Information Visualization systems will become consensual. It is also designed to help information visualization systems adhere to the best practices to provide a higher level of interoperability and leverage the domain of visual analytics

Learning Analytics Dashboard for Teaching with Twitter

As social media takes root in our society, more University instructors are incorporating platforms like Twitter into their classroom. However, few of the current Learning Analytics (LA) systems process social media data for instructional interventions and evaluation. As a result, instructors who are using social media cannot easily assess their students’ learning progress or use the data to adjust their lessons in real time. We surveyed 54 university instructors to better understand how they use social media in the classroom; we then used these results to design and evaluate our own Twitter-centric LA dashboard. The overarching goals for this project were to 1) assist instructors in determining whether their particular use of Twitter met their teaching objectives, and 2) help system designers navigate the nuance of designing LA dashboards for social media platforms

The Evidence Hub: harnessing the collective intelligence of communities to build evidence-based knowledge

Conventional document and discussion websites provide users with no help in assessing the quality or quantity of evidence behind any given idea. Besides, the very meaning of what evidence is may not be unequivocally defined within a community, and may require deep understanding, common ground and debate. An Evidence Hub is a tool to pool the community collective intelligence on what is evidence for an idea. It provides an infrastructure for debating and building evidence-based knowledge and practice. An Evidence Hub is best thought of as a filter onto other websites — a map that distills the most important issues, ideas and evidence from the noise by making clear why ideas and web resources may be worth further investigation. This paper describes the Evidence Hub concept and rationale, the breath of user engagement and the evolution of specific features, derived from our work with different community groups in the healthcare and educational sector

Understanding Communication Patterns in MOOCs: Combining Data Mining and qualitative methods

Massive Open Online Courses (MOOCs) offer unprecedented opportunities to learn at scale. Within a few years, the phenomenon of crowd-based learning has gained enormous popularity with millions of learners across the globe participating in courses ranging from Popular Music to Astrophysics. They have captured the imaginations of many, attracting significant media attention - with The New York Times naming 2012 "The Year of the MOOC." For those engaged in learning analytics and educational data mining, MOOCs have provided an exciting opportunity to develop innovative methodologies that harness big data in education.Comment: Preprint of a chapter to appear in "Data Mining and Learning Analytics: Applications in Educational Research

Exploring engagement profiling in MOOCs through Learning Analytics: The Open edX Case

The enormous amount of data being generated daily, requires effective and efficient ways of processing and analysing in order to extract useful information and form meaningful conclusions. Learning Analytics is a set of methodologies and practices that uncover such information from educational data. The research in this thesis explores the addition of a Learning Analytics feature to the context of a Learning Analytics tool that aids instructors using the online Massive Open Online Course (MOOC) platform, Open edX. This is done through the development and evaluation of a working artefact that supports profiling of students according to their activity throughout the course, alongside the visualizations, which represent said activity. As a result, the thoroughly demonstrated process of the artefact creation and feedback collection from the instructors shows the potential of Learning Analytics methods when applied to Open edX tracking data. Several practical features for creating different engagement groups, together with the visualizations, are conceptualized, implemented and evaluated, and are positively assessed by the target group of instructors. In addition, the challenges that were encountered in the period of the development, are presented, together with the suggestions to overcome them. Finally, a few extra features are outlined for future work, which could expand the existing functionality even more and bring additional knowledge to this research area.Master's Thesis in Information ScienceINFO390MASV-INF

Learning within Digital Media: Investigating the Relationships Between Student Citation Networks, Assignment Structures, and Learning Outcomes

Students are comfortable sharing digital content with others, yet the effect of sharing of digital media for learning remains largely unexplored. Building on research in social network analysis and learning analytics, this research explores the use and sharing of digital media in learning activities, analyzing the effects of the design of the learning activities on the resulting networks of students and their cited resources, and exploring relationships between attributes of these citation networks and students’ perceptions of the learning outcomes. Results suggest that the extent to which an assignment is well-structured and converges towards a single solution positively influences the density and clustering coefficient of the resulting citation network, and that these network measures in turn have a positive influence on students’ perceptions of learning from the assignment

An algorithm and a tool for the automatic grading of MOOC learners from their contributions in the discussion forum

MOOCs (massive open online courses) have a built-in forum where learners can share experiences as well as ask questions and get answers. Nevertheless, the work of the learners in the MOOC forum is usually not taken into account when calculating their grade in the course, due to the difficulty of automating the calculation of that grade in a context with a very large number of learners. In some situations, discussion forums might even be the only available evidence to grade learners. In other situations, forum interactions could serve as a complement for calculating the grade in addition to traditional summative assessment activities. This paper proposes an algorithm to automatically calculate learners' grades in the MOOC forum, considering both the quantitative dimension and the relevance in their contributions. In addition, the algorithm has been implemented within a web application, providing instructors with a visual and a numerical representation of the grade for each learner. An exploratory analysis is carried out to assess the algorithm and the tool with a MOOC on programming, obtaining a moderate positive correlation between the forum grades provided by the algorithm and the grades obtained through the summative assessment activities. Nevertheless, the complementary analysis conducted indicates that this correlation may not be enough to use the forum grades as predictors of the grades obtained through summative assessment activities.This work was supported in part by the FEDER/Ministerio de Ciencia, Innovación y Universidades;Agencia Estatal de Investigación, through the Smartlet Project under Grant TIN2017-85179-C3-1-R, and in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307, a project which is co-funded by the European Structural Funds (FSE and FEDER). Partial support has also been received from the European Commission through Erasmus+ Capacity Building in the Field of Higher Education projects, more specifically through projects LALA (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), InnovaT (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP), and PROF-XXI (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP)

Representation of virtual choreographies in learning dashboards of interoperable LMS analytics

Learning management systems (LMS) collect a large amount of data from user interaction, and it isn't easy to analyze this data in a reliable and context-independent manner. This research seeks to comprehend how virtual choreographies can be represented in interoperable LMS analytics dashboards. In order to gain a better understanding of the problem, this objective has been divided into three sub-goals: determining which interactions can be gathered from LMS contexts, identifying virtual choreographies from LMS logs, and representing virtual choreographies in learning dashboards. To achieve these objectives, we first conducted a Systematic Literature Review to comprehend the behaviors and interactions other authors have investigated in LMS contexts. Then, by applying these findings to this dissertation's case study, a methodical procedure for extracting valuable choreographies from the logs was outlined. The Design Science Research methodology was then applied to transforming logs into virtual choreographies and their representation in learning dashboards. It was implemented two services: one responsible for identifying virtual choreographies from data logs and transforming the logs into statements, recipes, and choreographies, following xAPI specification elements; and the other translates the information from the backend service into dashboard visualizations, allowing the user to view representations for statements, recipes, choreographies, and various visualizations. These artifacts provide a new flexible and cost-efficient solution for the identification of virtual choreographies, thereby facilitating the widespread adoption of their use