Adaptive E-assessment Research Articles

An Adaptive E-Assessment for Self-Learning: A Sustainable Education Model in the Post COVID-19 ERA of Digital Media

An Adaptive E-Assessment for Self-Learning: A Sustainable Education Model in the Post COVID-19 ERA of Digital Media

Combination of fuzzy and cognitive theories for adaptive e-assessment

A crucial factor for successful educational results in computer-based educational systems and e-learning systems is the learner’s assessment. Assessment is more effective when it is tailored to each individual student’s learning needs and abilities. Therefore, a significant research challenge is to create tests that include exercises/questions/activities etc., which conform to each learner’s knowledge level and learning needs and abilities. This goal constitutes the need for creating adaptive tests. However, the area of adaptive e-assessment has not yet been explored sufficiently and thus there is scope for a lot of improvement. To this end, in this paper we present a novel solution for adaptive e-assessment. The novelty and significance lie in the blending of fuzzy logic and cognitive theories for further enhancing the personalization and adaptivity in e-assessment. Particularly, fuzzy sets are used to describe the knowledge level of students in a more realistic way. Furthermore, the cognitive theory of Revised Bloom Taxonomy is used to express the learning objectives that are required to be assessed through the created test. In addition, a fuzzy rule-based reasoner, which decides about the number and the difficulty level of the test items that have to be included into the created personalized test for each level of the Revised Bloom Taxonomy, is used. The fuzzy rules are applied to the fuzzy sets that describe the learners’ knowledge level. For the formation of the fuzzy sets and rules, the opinion of several tutors, holding experience in the educational process and instruction, was taken into consideration. The created adaptive test comprises distinct test items based on the individual learning needs of each student. The presented method has been used in two tutoring systems and has been fully evaluated. The evaluation results show great accuracy in the selection of test items for each individual student.

Performance Evaluation of Students in Adaptive E-Assessment using Structural Equation Modeling

In today’s competitive world, students have to learn many skills and assessments are designed to judge their abilities in a more scientific way. Adaptive E-Assessment is a popular mode of evaluating the performance of the learners. It is a reliable approach and is commonly used by educational institutions around the world. An adaptive strategy using multiple choice questions for conducting E-assessment has been formulated for assessing the knowledge level of students. Various statistical measures were used to compare the performance of the students. This study uses structural equation modelling (SEM) to identify the relationship between the various factors contributing to the test score and to assess the reliability of the relationships between these factors.

A Bayesian Classification Approach for Handling Uncertainty in Adaptive E-Assessment

New technologies have provided the educational field with innovations that allow significant improvement in the teaching learning process. The development of information technology has increased the popularity of Web based education, ; particularly E-assessment This paper provides an overview of Bayesian Network and its application to handle uncertainty in adaptive E-assessment. An adaptive assessment system was realized in PHP and MYSQL. The empirical data was collected and the relationship between the response time and the assessment grading was established. The classifier accuracy obtained using Bayesian classifier was compared with C4.5 Decision tree algorithm and Naive Bayes classifier. The superiority of Bayesian algorithm over the well known algorithms in handling uncertainty of adaptive assessment data is established in this paper.

An Adaptive E-Assessment Grading (Aeag) Model for Performance Evaluation

An Adaptive E-Assessment Grading (Aeag) Model for Performance Evaluation