Archive for the ‘Analytics’ Category

Data, AI and Collaboration Top Higher Ed Tech Trends

Microsoft’s vice president for worldwide education, Anthony Salcito, highlights the future of higher education technology. EdTech Focus on Higher Education 

Research Project Figures Out How to Crowdsource Predictive Models

An MIT research project has come up with a way to crowdsource development of features for use in machine learning. Groups of data scientists contribute their ideas for this “feature engineering” into a collaboration tool named “FeatureHub.” The idea, according to lead researcher Micah Smith, is to enable contributors to spend a few hours reviewing […]

Portfolio, text, data, page

Designing and integrating reusable learning objects for meaningful learning: Cases from a graduate programme

E-learning quality depends on sound pedagogical integration between the content resources and lesson activities within an e-learning system. This study proposes that a meaningful learning with technology framework can be used to guide the design and integration of content resources with e-learning activities in ways that promote learning experiences, characterised by five dimensions: active, constructive, […]

Designing for student-facing learning analytics

Despite a narrative that sees learning analytics (LA) as a field that aims to enhance student learning, few student-facing solutions have emerged. This can make it difficult for educators to imagine how data can be used in the classroom, and in turn diminishes the promise of LA as an enabler for encouraging important skills such […]

RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

Various forms of Peer-Learning Environments are increasingly being used in postsecondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender […]

Closing the loop: Automated data-driven cognitive model discoveries lead to improved instruction and learning gains

As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it yields better […]

Recommending Learning Activities in Social Network Using Data Mining Algorithms

In this paper, we show how data mining algorithms (e.g. Apriori Algorithm (AP) and Collaborative Filtering (CF)) is useful in New Social Network (NSN-AP-CF). “NSN-AP-CF” processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the users through mining the frequent episodes by the Apriori algorithm. Finally, it […]

A Learning Analytics Tool for Usability Assessment in Moodle Environments

The use of analytics technologies is increasingly successful in the e-learning domain. In this paper, we propose a novel model aiming at evaluating usability of interfaces adopted by Learning Management Systems and Massive Open Online Courses platforms based on the comparison between desktop and mobile versions, using specific native indicators. The indicators obtained from log […]

An Instructor Learning Analytics Implementation Model

With the widespread use of learning analytics tools, there is a need to explore how these technologies can be used to enhance teaching and learning. Little research has been conducted on what human processes are necessary to facilitate meaningful adoption of learning analytics. The research problem is that there is a lack of evidence-based guidance […]

Using Learning Analytics for Preserving Academic Integrity

This paper presents the results of integrating learning analytics into the assessment process to enhance academic integrity in the e-learning environment. The goal of this research is to evaluate the computational-based approach to academic integrity. The machine-learning based framework learns students’ patterns of language use from data, providing an accessible and non-invasive validation of student […]