Evaluating Web Based Instructional Models Using Association Rule Mining

García, Enrique; Romero, Cristóbal; Ventura, Sebastián; Castro, Carlos de

doi:10.1007/978-3-642-02247-0_5

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…There has also been interest in modeling the user by developing and tuning cognitive models to represent their skills and declarative knowledge (Frias-Martinez et al, 2006). Additionally, instructors and developers of learning content have seen the benefit in the utilization of EDM for constructing coursewares (Garcia et al, 2009). Lastly, using the vast data available, parameters to probabilistic models can be inferred (also known as parameter estimation) to determine the probability of an event of interest to occur (Wauters et al, 2011).…”

Section: Applications Of Edmmentioning

confidence: 99%

Educational Data Mining and Learning Analytics for Improving Online Learning Environments

Paredes

Siegle

Hsiao

et al. 2020

Proceedings of the Human Factors and Ergonomics Society Annual

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The proliferation of educational technology systems has led to the advent of a large number of datasets related to learner interaction. New fields have emerged which aim to use this data to identify interventions that could help the learners become efficient and effective in their learning. However, these systems have to follow user-centered design principles to ensure that the system is usable and the data is of high quality. Human factors literature is limited on the topics regarding Educational Data Mining (EDM) and Learning Analytics (LA). To develop improved educational systems, it is important for human factors engineers to be exposed to these data-oriented fields. This paper aims to provide a brief introduction to the fields of EDM and LA, discuss data visualization and dashboards that are used to convey results to learners, and finally to identify where human factors can aid other fields.

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Section: Applications Of Edmmentioning

confidence: 99%

Educational Data Mining and Learning Analytics for Improving Online Learning Environments

Paredes

Siegle

Hsiao

et al. 2020

Proceedings of the Human Factors and Ergonomics Society Annual

View full text Add to dashboard Cite

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“…This can be an arduous task and therefore educational recommender systems are used in order to help a teacher in this task. Garcia et al, (2010) described a recommender system oriented to suggesting the most appropriate modifications to a teacher in order to improve the effectiveness of the course. A cyclical methodology to develop and carry out the maintenance of web-based courses was proposed with a distributed rule mining system in order to discover information in form of IF-THEN recommendation rules about the web courses.…”

Section: Collaborative Filtering (Cf) Is a Commonmentioning

confidence: 99%

Providing Personalized Services to Users in a Recommender System

Ojokoh

Oduwobi

2015

International Journal of Web-Based Learning and Teaching Technologies

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Instructors recommend learning materials to a class of students not minding the learning ability and reading habit of each student. Learners are finding it problematic to make a decision about which available learning materials best meet their situation and will be beneficial to their course of study. In order to address this challenge, a new e-learning material recommender system that is able to recommend quality items to learners individually is required. The aim of this work is to develop a Personalized Recommender System that switches between Content-based and Collaborative filtering techniques, with an objective to design an algorithm to recommend electronic library materials, as well as personalize recommendations to both new and existing users. Experiments were conducted with evaluations showing that the recommender system was most effective when content-based filtering and collaborative filtering were used to recommend items for new users and existing users respectively, and still achieve personalization.

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“…They provide students with an additional platform for learning. For instance, learning management systems (LMSs) enable students, in an out-of-classroom setting, to choose their own learning pathway through a course and share knowledge through tools such as forums, emails, chat rooms and discussion groups (García et al 2009). The accessibility of educational resources, therefore, is essential so that leaners with disabilities can have equal opportunity.…”

Section: Introductionmentioning

confidence: 99%

Inclusion, access, and accessibility of educational resources in higher education institutions: exploring the Ethiopian context

Beyene

Mekonnen

2020

International Journal of Inclusive Education

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The right of persons with disabilities for equal access to education and educational resources is enshrined by international and country-specific anti-discrimination laws. Taking the Ethiopian context as an example, this paper sought to identify barriers of access to educational resources and explored ways for removing them. Seventeen students with visual impairments studying at Hawassa University were selected for semi-structured interviews. Moreover, five individuals working at the disability centre and the university library were interviewed. The results of the interviews were analysed thematically using the International Classification of Functioning, Disabilities and Health (ICF) as a framework. Access and accessibility problems that emanate from the learners' diverse background, lack of educational resources in alternative formats, lack of institutional tools (policy, procedure, guidelines, etc.) to bridge the gap between law and practice, and the digital divide were among the problems identified and discussed. At the end, the paper showed how libraries, revitalised as learning and information commons, could help to ensure the accessibility of educational resources and help learners with disabilities to acquire skills that may help them in their studies and their future undertakings.

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Evaluating Web Based Instructional Models Using Association Rule Mining

Cited by 5 publications

References 6 publications

Educational Data Mining and Learning Analytics for Improving Online Learning Environments

Educational Data Mining and Learning Analytics for Improving Online Learning Environments

Providing Personalized Services to Users in a Recommender System

Inclusion, access, and accessibility of educational resources in higher education institutions: exploring the Ethiopian context

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