Comparison of Different Machine Learning Algorithms for the Initialization of Student Knowledge Level in a Learner Model-Based Adaptive E-Learning System

Citation:
Oboko RO, Wagacha PW, Omwenga E. "Comparison of Different Machine Learning Algorithms for the Initialization of Student Knowledge Level in a Learner Model-Based Adaptive E-Learning System.". 2009.

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Web-based learning systems give students the freedom to determine what to study based on each individual student’s learning goals. These systems support students in constructing their own knowledge for solving problems at hand. However, in the absence of instructors, students often need to be supported as they learn in ways that are tailored to suit a specific student. Adaptive web-based learning systems are suited to such situations. In order for an adaptive learning system to be able to provide learning support, it needs to build a model of each individual student and then to use the attribute values for each student as stored in the student model to determining the kind of learning support that is suitable for each student. Examples of such attributes are student knowledge level, learning styles, student errors committed during learning, the student’s program of study, gender and number of programming languages learned by the student of programming. There are two important issues about the use of student models. Firstly, how to initialize the attributes in the student models and secondly, how to update the attribute values of the student model as students interact with the learning system. With regard to initialization of student models, one of the approaches used is to input into a machine learning algorithm attribute values of students who are already using the system and who are similar (hence called neighbors) to the student whose model is being initialized. The algorithm will use these values to predict initial values for the attributes of a new student. Similarity among students is often expressed as the distance from one student to another. This distance is often determined using a heterogeneous function of Euclidean and Overlap measures (HOEM). This paper reports the results of an investigation on how HOEM compares to two different variations of Value Difference Metric (VDM) combined with the Euclidean measure (HVDM) using different numbers of neighbors. An adaptive web-based learning system teaching object oriented programming was used. HOEM was found to be more accurate than the two variations of HVDM

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