SMS Based E-assessments Enabling better Student Engagement, Evaluation and Recommendation Services in E-learning Making Use of Fuzzy Rules and Course Ontologies

Antony, Joms; Ramakrishnan, Sandhya; Joy, Manoj T.; Thottupuram, Rubin

doi:10.1109/t4e.2012.9

Cited by 5 publications

(1 citation statement)

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“…In education, FL was used in expert systems to evaluate students' academic performance [42], to analyze students' lifestyle [43], providing interactive recommendations with interactive and meaningful engagement in innovative e-learning [44], and to assist teachers, examiners and evaluators in managing uncertainties in the decision making process in evaluating the students [45].…”

Section: Fuzzy Logicmentioning

confidence: 99%

A Career Track Recommender System for Senior High School Students using Fuzzy Logic

B¹

2019

IJATCSE

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Rule-based algorithms are mostly used in recommender systems but still, cannot address the issue of uncertainty in decision making particularly to senior high school students in choosing the right career track because of numerous influential factors that may affect their decisions. That is why a career track recommender system using fuzzy logic has been developed to address this issue. In this paper, the significant factors that is most influential to the decision of the students as best attributes were determined using feature selection filtering techniques and used as crisp inputs. The result shows that the developed fuzzy model performs a high predictive accuracy based on the computed mean absolute error (MAE) and root-mean-square error (RMSE) scores which decreases from the training, to the validation and test sets. The recommendation returns the best possible result based on the computed normalized discounted cumulative gain (nDCG) which is 0.948 from the desired to the actual preference of students which is almost near to 1.0. With these, the developed recommender system is highly recommended as perceived by the users in terms of usability, maintainability and portability.

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Section: Fuzzy Logicmentioning

confidence: 99%