Archive for the ‘Adpative Systems’ Category

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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 […]

Download Report: Students say colleges lag in providing personalized digital experience

Tech-savvy reputation is a key factor for 9 in 10 students when applying to colleges, but schools need to do more, Ellucian study concludes.​ Edscoop

EdSurge Live: Who Controls AI in Higher Ed, And Why It Matters (Part 1)

It’s a pivotal time for artificial intelligence in higher education. More instructors are experimenting with adaptive-learning systems in their classrooms. College advising systems are trying to use predictive analytics to increase student retention. And the infusion of algorithms is leading to questions—ethical questions and practical questions and philosophical questions—about how far higher education should go […]

The Influence of a Pedagogical Agent on Learners’ Cognitive Load

According to cognitive load theorists, the incorporation of extraneous features, such as pedagogical agents, into the learning environment can introduce extraneous cognitive load and thus interfere with learning outcome scores. In this study, the influence of a pedagogical agent’s presence in an instructional video was compared to a video that did not contain a pedagogical […]

The Role of Learner Characteristics in the Adaptive Educational Hypermedia Systems: The Case of the MATHEMA

The aim of this paper is to explore the characteristics of the learners used by the developed adaptive educational hypermedia systems to date to draw conclusions about their relation to the adaptation techniques they use and to be explained the rationale for selecting of the learners’ characteristics used by the adaptive educational hypermedia system MATHEMA […]

Download Report: Informing Progress: Insights on Personalized Learning Implementation and Effects

Informing Progress: Insights on Personalized Learning Implementation and Effects The basic concept of personalized learning (PL) — instruction that is focused on meeting students’ individual learning needs while incorporating their interests and preferences — has been a longstanding practice in U.S. K–12 education. Options for personalization have increased as personal computing devices have become increasingly […]