Maomi Ueno

Bees Algorithm for Construction of Multiple Test Forms in E-Testing

The purpose of this research is to automatically construct multiple equivalent test forms that have equivalent qualities indicated by test information functions based on item response theory. There has been a trade-off in previous studies between the computational costs and the equivalent qualities of test forms. To alleviate this problem, we propose an automated system of test construction based on the Bees Algorithm in parallel computing. We demonstrate the effectiveness of the proposed system through various experiments.

Animated pedagogical agent based on decision tree for e-learning

This paper proposes a LMS (learning management system) with intelligent agent to provide effective adaptive messages to a learner. The unique features of this paper are shown as follows: The agent system proposed in this paper has a learner model, which is automatically and continually constructed by applying the decision tree model constructed from the learning histories data stored in the data-base. The constructed leaner model predicts a learners future final status (1. Failed, 2. Abandon, 3. Successful, 4.Excellent) using his/her current learning history data. The constructed leaner model becomes more exact as the amount of data accumulated in the database increases. The agent system presents the optimal instructional message based on the learners predicted future state. The agent provides some attention cues according to Ueno (2004) at the timing when a learner begins to be bored with his/her learning. In addition, this paper demonstrates the effectiveness of this system through actual e-learning classes.

Item Response Theory for Peer Assessment

As an assessment method based on a constructivist approach, peer assessment has become popular in recent years. However, in peer assessment, a problem remains that reliability depends on the rater characteristics. For this reason, some item response models that incorporate rater parameters have been proposed. Those models are expected to improve the reliability if the model parameters can be estimated accurately. However, when applying them to actual peer assessment, the parameter estimation accuracy would be reduced for the following reasons. 1) The number of rater parameters increases with two or more times the number of raters because the models include higher-dimensional rater parameters. 2) The accuracy of parameter estimation from sparse peer assessment data depends strongly on hand-tuning parameters, called hyperparameters. To solve these problems, this article presents a proposal of a new item response model for peer assessment that incorporates rater parameters to maintain as few rater parameters as possible. Furthermore, this article presents a proposal of a parameter estimation method using a hierarchical Bayes model for the proposed model that can learn the hyperparameters from data. Finally, this article describes the effectiveness of the proposed method using results obtained from a simulation and actual data experiments.

Analysis of the Advantages of Using Tablet PC in e-Learning

This paper relates to the effect of tablet PCs in e-learning. We performed analysis based on the “dual channel model”, which models the information processing capabilities of humans. More specifically, we provided paper media, keyboards, pen tablets, and tablet PCs as input devices used for annotations during e-learning, measured the gaze point of each learner by an eye-mark recorder, and evaluated each device by setting memory and comprehension tests, giving questionnaires, and evaluating the note-taking. As a result, we have shown that the use of tablet PCs in e-learning 1) enables concentration on the content, 2) reduces the extraneous cognitive load imposed by making annotations, 3) increases learners’ comprehension and memory retention, and 4) enables efficient note-taking, thus increasing the accuracy of notes as learning aids.

Student models construction by using information criteria

Proposes a method of constructing student models for intelligent tutoring systems (ITSs) by using information criteria. This proposal provides a method to automatically construct the optimum student model from data. The main problem when traditional information criteria are employed to construct a model is that a large amount of data, which is difficult to obtain in actual school situations, needs to be obtained. This paper proposes a new criterion for using a smaller amount of data by utilizing a teachers expert knowledge. Concretely, (1) the general predictive distribution is derived, and (2) a method of determining the hyper-parameters by using a teachers expert knowledge is proposed. Finally, some Monte Carlo experiments comparing some information criteria [BIC (Bayesian information criterion), ABIC (Akaikes extension of BIC), MDL (minimum description length), and the exact predictive distribution] are performed. The results show that the proposed method provides the best performance.

Maximum Clique Algorithm and ItsApproximation for UniformTest Form Assembly

Educational assessments occasionally require uniform test forms for which each test form comprises a different set of items, but the forms meet equivalent test specifications (i.e., qualities indicated by test information functions based on item response theory). We propose two maximum clique algorithms (MCA) for uniform test form assembly. The proposed methods can assemble uniform test forms with allowance of overlapping items among uniform test forms. First, we propose an exact method that maximizes the number of uniform test forms from an item pool. However, the exact method presents computational cost problems. To relax those problems, we propose an approximate method that maximizes the number of uniform test forms asymptotically. Accordingly, the proposed methods can use the item pool more efficiently than traditional methods can. We demonstrate the efficiency of the proposed methods using simulated and actual data.

A Fast Clique Maintenance Algorithm for Optimal Triangulation of Bayesian Networks

The junction tree algorithm is currently the most popular algorithm for exact inference on Bayesian networks. To improve the time and space complexity of the junction tree algorithm, we must find an optimal total table size triangulations. For this purpose, Ottosen and Vomlel proposed a depth-first search DFS algorithm for optimal triangulation. They also introduced several techniques for improvement of the DFS algorithm, including dynamic clique maintenance and coalescing map pruning. However, their dynamic clique maintenance might compute some duplicate cliques. In this paper, we propose a new dynamic clique maintenance that only computes the cliques that contain a new edge. The new approach explores less search space and runs faster than the Ottosen and Vomlel method does. Some simulation experiments show that the new dynamic clique maintenance improved the running time of the optimal triangulation algorithm.

Goal-Based Messages Recommendation Utilizing Latent Dirichlet Allocation

Observing various learning goals from peers allows learners to specify new objectives and sub-goals to improve their personal experience. Setting goals for learning enhances motivation and performance. However an unrelated goal might lead to poor outcome. Hence learners have divergent objectives for a same learning experience. Latent Dirichlet Allocation (LDA) is a model considering documents as a mixture of topics. This study then proposed a recommendation model based on LDA, able to determine distinct categories of goals within a single dataset. Results focused on a dataset of 10 learning subjects and over 16,000 goal-based Twitter messages. It showed (1) different goal categories and (2) the correlation between the LDA parameter for the number of topics and the type of subject. Evaluations of goal attributes also showed an increase of goal specificity, commitment and self-confidence after observing different types of goals from peers.

A SCORM-compliant Learning Management System that Enhances Learning By Managing the Learning Itself

A SCORM-compliant learning management system (LMS) has been developed that enhances learning by effectively and efficiently managing the learning itself. First, a SCORM-LST was developed by adding to SCORM (sharable content object reference model) a framework that describes facilitation corresponding to the learners state of learning and describes the learning state transitions. This makes it possible to describe collaborative learning, assessment, and facilitation for multiple users. Next, a SCORM-compliant LMS (SALMS: SCORM-compliant adaptive LMS) based on the SCORM-LST was developed. SALMS interprets SCORM-LST code and changes the user interface to match the learners state of learning. It also manages the learning so that it matches the designers intentions.

Intelligent tutoring system based on belief networks

This paper proposes a new Intelligent Tutoring System based on Belief networks. The unique features of this system are as follows: 1. The Student model is represented by belief networks which has a Dirichret distribution as a prior, 2. The structure of the student model is constructed from the data-base by maximizing the predict distribution, and 3. Instruction strategies are described as utility functions in decision making theory. Especially, in this paper, the utility function is assumed as the expected learning effects defined by EVII (Expected Value of Instruction Information), and it is shown that it explains sufficiently teacher flexible behavior and interactive instruction process.