A semi-automatic computer-aided assessment framework for primary mathematics

Assessment and feedback processes shape students behaviour, learning and skill development. Computer-aided assessments are increasingly being used to support problem-solving, marking and feedback activities. However, many computer-aided assessment environments only replicate traditional pencil-and-paper tasks. Attention is on grading and providing feedback on the final product of assessment tasks rather than the processes of problem solving. Focusing on steps and problem-solving processes can help teachers to diagnose strengths and weaknesses, discover problem-solving strategies, and to provide appropriate feedback to students. This thesis presents a semi-automatic framework for capturing and marking students solution steps in the context of elementary school mathematics. The first focus is on providing an interactive touch-based tool called MuTAT to facilitate interactive problem solving for students. The second focus is on providing a marking tool named Marking Assistant which utilises the case-based reasoning artificial intelligence methodology to carry out marking and feedback activities more efficiently and consistently. Results from studies carried out with students showed that the MuTAT prototype tool was usable, and performance scores on it were comparable to those obtained when paper-and-pencil was used. More importantly, the MuTAT provided more explicit information on the problem-solving process, showing the students thinking. The captured data allowed for the detection of arithmetic strategies used by the students. Exploratory studies conducted using the Marking Assistant prototype showed that 26% savings in marking time can be achieved compared to traditional paper-and-pencil marking and feedback. The broad feedback capabilities the research tools provided can enable teachers to evaluate whether intended learning outcomes are being achieved and so decide on required pedagogical interventions. The implications of these results are that innovative CAA environments can enable more direct and engaging assessments which can reduce staff workloads while improving the quality of assessment and feedback for students

Proceedings of the 3rd Workshop on Social Information Retrieval for Technology-Enhanced Learning

Learning and teaching resource are available on the Web - both in terms of digital learning content and people resources (e.g. other learners, experts, tutors). They can be used to facilitate teaching and learning tasks. The remaining challenge is to develop, deploy and evaluate Social information retrieval (SIR) methods, techniques and systems that provide learners and teachers with guidance in potentially overwhelming variety of choices. The aim of the SIRTEL’09 workshop is to look onward beyond recent achievements to discuss specific topics, emerging research issues, new trends and endeavors in SIR for TEL. The workshop will bring together researchers and practitioners to present, and more importantly, to discuss the current status of research in SIR and TEL and its implications for science and teaching

Optimizing E-Management Using Web Data Mining

Today, one of the biggest challenges that E-management systems face is the explosive growth of operating data and to use this data to enhance services. Web usage mining has emerged as an important technique to provide useful management information from user's Web data. One of the areas where such information is needed is the Web-based academic digital libraries. A digital library (D-library) is an information resource system to store resources in digital format and provide access to users through the network. Academic libraries offer a huge amount of information resources, these information resources overwhelm students and makes it difficult for them to access to relevant information. Proposed solutions to alleviate this issue emphasize the need to build Web recommender systems that make it possible to offer each student with a list of resources that they would be interested in. Collaborative filtering is the most successful technique used to offer recommendations to users. Collaborative filtering provides recommendations according to the user relevance feedback that tells the system their preferences. Most recent work on D-library recommender systems uses explicit feedback. Explicit feedback requires students to rate resources which make the recommendation process not realistic because few students are willing to provide their interests explicitly. Thus, collaborative filtering suffers from “data sparsity” problem. In response to this problem, the study proposed a Web usage mining framework to alleviate the sparsity problem. The framework incorporates clustering mining technique and usage data in the recommendation process. Students perform different actions on D-library, in this study five different actions are identified, including printing, downloading, bookmarking, reading, and viewing the abstract. These actions provide the system with large quantities of implicit feedback data. The proposed framework also utilizes clustering data mining approach to reduce the sparsity problem. Furthermore, generating recommendations based on clusters produce better results because students belonging to the same cluster usually have similar interests. The proposed framework is divided into two main components: off-line and online components. The off-line component is comprised of two stages: data pre-processing and the derivation of student clusters. The online component is comprised of two stages: building student's profile and generating recommendations. The second stage consists of three steps, in the first step the target student profile is classified to the closest cluster profile using the cosine similarity measure. In the second phase, the Pearson correlation coefficient method is used to select the most similar students to the target student from the chosen cluster to serve as a source of prediction. Finally, a top-list of resources is presented. Using the Book-Crossing dataset the effectiveness of the proposed framework was evaluated based on sparsity level, and Mean Absolute Error (MAE) regarding accuracy. The proposed framework reduced the sparsity level between (0.07% and 26.71%) in the sub-matrices, whereas the sparsity level is between 99.79% and 78.81% using the proposed framework, and 99.86% (for the original matrix) before applying the proposed framework. The experimental results indicated that by using the proposed framework the performance is as much as 13.12% better than clustering-only explicit feedback data, and 21.14% better than the standard K Nearest Neighbours method. The overall results show that the proposed framework can alleviate the Sparsity problem resulting in improving the accuracy of the recommendations

Integration of Reciprocal Teaching-ICT Model To Improve Students’Mathematics Critical Thinking Ability

This research examines the effectiveness on how mathematics teachers have begun to integrate information and communication technology (ICT) with reciprocal teaching model to improve students’ mathematics critical thinking ability into seventh junior high school classroom practice. This study was experimental research with a quasi-experimental design. The samples of the study are 36 students for classroom experiments and 36 students for classroom control. The instruments employed in this study were pre-test and post-test. All the instruments are made in essays forms. The data were analyzed by using descriptive statistics. Based on the research findings, it was gotten that (1) the development of teaching instructional multimedia of the seven grade students of junior high school; (2) the improvement of students’ mathematics critical thinking ability in experimental class; (3) the aspect of attractiveness shows that the developed instructional multimedia was very interesting; and (4) reciprocal learning has good impact on students’ mathematics critical thinking ability