Identifying Difficult exercises in an eTextbook Using Item Response Theory and Logged Data Analysis

The growing dependence on eTextbooks and Massive Open Online Courses (MOOCs) has led to an increase in the amount of students' learning data. By carefully analyzing this data, educators can identify difficult exercises, and evaluate the quality of the exercises when teaching a particular topic. In this study, an analysis of log data from the semester usage of the OpenDSA eTextbook was offered to identify the most difficult data structure course exercises and to evaluate the quality of the course exercises. Our study is based on analyzing students' responses to the course exercises. We applied item response theory (IRT) analysis and a latent trait mode (LTM) to identify the most difficult exercises .To evaluate the quality of the course exercises we applied IRT theory. Our findings showed that the exercises that related to algorithm analysis topics represented the most difficult exercises, and there existing six exercises were classified as poor exercises which could be improved or need some attention.Comment: 6 pages,5 figure

A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse

The profusion of existing ontologies in different domains has made reusing ontologies a best practice when developing new ontologies. The ontology reuse process reduces the expensive cost of developing a new ontology, in terms of time and effort, and supports semantic interoperability. Existing ontology development tools do not assist in the recommendation of ontologies or their concepts to be reused. Also, existing ontology recommendation tools could suggest whole ontologies covering a set of input keywords without referring to which parts of them (e.g., concepts) can be reused. In this paper, we propose an effective ontology recommendation system that helps the user in the iterative development and reuse of ontologies. The system allows the user to provide explicit preferences about the new ontology, and iteratively guides the user to parts from existing ontologies which match his preferences for reuse. Finally, we developed a prototype of our ontology recommendation system and conducted a user-based evaluation to assess the effectiveness of our approach

Embedding-Based Deep Neural Network and Convolutional Neural Network Graph Classifiers

One of the most significant graph data analysis tasks is graph classification, as graphs are complex data structures used for illustrating relationships between entity pairs. Graphs are essential in many domains, such as the description of chemical molecules, biological networks, social relationships, etc. Real-world graphs are complicated and large. As a result, there is a need to find a way to represent or encode a graph’s structure so that it can be easily utilized by machine learning models. Therefore, graph embedding is considered one of the most powerful solutions for graph representation. Inspired by the Doc2Vec model in Natural Language Processing (NLP), this paper first investigates different ways of (sub)graph embedding to represent each graph or subgraph as a fixed-length feature vector, which is then used as input to any classifier. Thus, two supervised classifiers—a deep neural network (DNN) and a convolutional neural network (CNN)—are proposed to enhance graph classification. Experimental results on five benchmark datasets indicate that the proposed models obtain competitive results and are superior to some traditional classification methods and deep-learning-based approaches on three out of five benchmark datasets, with an impressive accuracy rate of 94% on the NCI1 dataset