Students’ characteristics of student model in intelligent programming tutor for learning programming: a systematic literature review

This study describes preliminary results of a research related to Intelligent Programming Tutor (IPT) which is derived from Intelligent Tutoring System (ITS). The system architecture consists of four models. However, in this study student model mainly student characteristic was focused. From literature, 44 research articles were identified from a number of digital databases published between 1997 to 2022 base on systematic literature review (SLR) method. The findings show that the majority 48% of IPT implementation focuses on knowledge and skills. While 52% articles focused on a combination of two to three student characteristics where one of the combinations is knowledge and skill. When narrow down, 25% focused on knowledge and skills with errors or misconceptions; 4% focused on knowledge and skill with cognitive features; 5% focused focus on knowledge and skill with affective features; 2% focused on knowledge and skill with motivation; and 9% based on knowledge and skill with learning style and learning preferences as students’ characteristics to build their student model. Whereas 5% focused on a combination of three student characters which are knowledge and skill with cognitive and affective features and 2% focused on knowledge and skill with learning styles and learning preferences and motivation as students’ characteristics to construct the tutoring system student model. To provide an appropriate tutoring system for the students, students’ characteristic needs to decide for the student model before developing the tutoring system. From the findings, it can say that knowledge and skills is an essential students’ characteristic used to construct the tutoring system student model. Unfortunately, other students’ characteristic is less considered especially students’ motivation

Fuzzy-based user modelling for motivation assessment in programming learning adaptive web-based education systems

Learning programming is not an easy task and students often find this subject difficult to understand and pass. One way to improve students’ knowledge in programming is by using Intelligent Tutoring System (ITS) through Adaptive Web-Based Education Systems (AWBESs). The objective of ITS is to provide a personalized tutoring that is tailored to the student’s needs. User modelling is one of the key factors that can meet the ITS intended objectives. From the literature, it was discovered that motivation stands out as one of the critical students’ characteristics that need to be considered when designing a user model. However, from the previous studies, it was discovered that almost all the researchers and educators constructed the user model based on knowledge and skills as students’ characteristics. Thus, the aim of this study is to develop a user model based on students’ motivation known as the Motivation Assessment Model. This is a model that is able to assess students’ motivation level and deliver tutorial materials accordingly. The Motivation Assessment Model was developed based on Self-Efficacy theory that contributes to the fundamental motivation factor which influences students’ motivation during the learning process. Furthermore, to assess the motivation level, fuzzy logic technique was applied. A tutoring system was then developed based on the proposed model using ITS architecture and ADDIE instructional design model. In order to determine students’ knowledge level after using the tutoring system, pre- and post-tests were conducted on the controlled group and experimental group (30 and 31 students). The learning achievements between experimental group (mean = 3.00) and control group (mean = 2.00) indicated that the tutoring system is significantly more effective in improving students’ knowledge level compared to the traditional approach. A usability evaluation was also conducted whereby the effectiveness was evaluated at the number of errors (7.5%) and completion rate (86.5%); efficiency (mean = 4.85); satisfaction evaluated at task level (mean = 6.77) and test level (mean = 83.55). As a conclusion, the overall tutoring system usability results are accepted by students in the experimental group. The research contribution to knowledge is the development of the proposed Motivation Assessment Model for ITS

Automated Debugging Methodology for FPGA-based Systems

Electronic devices make up a vital part of our lives. These are seen from mobiles, laptops, computers, home automation, etc. to name a few. The modern designs constitute billions of transistors. However, with this evolution, ensuring that the devices fulfill the designer’s expectation under variable conditions has also become a great challenge. This requires a lot of design time and effort. Whenever an error is encountered, the process is re-started. Hence, it is desired to minimize the number of spins required to achieve an error-free product, as each spin results in loss of time and effort. Software-based simulation systems present the main technique to ensure the verification of the design before fabrication. However, few design errors (bugs) are likely to escape the simulation process. Such bugs subsequently appear during the post-silicon phase. Finding such bugs is time-consuming due to inherent invisibility of the hardware. Instead of software simulation of the design in the pre-silicon phase, post-silicon techniques permit the designers to verify the functionality through the physical implementations of the design. The main benefit of the methodology is that the implemented design in the post-silicon phase runs many order-of-magnitude faster than its counterpart in pre-silicon. This allows the designers to validate their design more exhaustively. This thesis presents five main contributions to enable a fast and automated debugging solution for reconfigurable hardware. During the research work, we used an obstacle avoidance system for robotic vehicles as a use case to illustrate how to apply the proposed debugging solution in practical environments. The first contribution presents a debugging system capable of providing a lossless trace of debugging data which permits a cycle-accurate replay. This methodology ensures capturing permanent as well as intermittent errors in the implemented design. The contribution also describes a solution to enhance hardware observability. It is proposed to utilize processor-configurable concentration networks, employ debug data compression to transmit the data more efficiently, and partially reconfiguring the debugging system at run-time to save the time required for design re-compilation as well as preserve the timing closure. The second contribution presents a solution for communication-centric designs. Furthermore, solutions for designs with multi-clock domains are also discussed. The third contribution presents a priority-based signal selection methodology to identify the signals which can be more helpful during the debugging process. A connectivity generation tool is also presented which can map the identified signals to the debugging system. The fourth contribution presents an automated error detection solution which can help in capturing the permanent as well as intermittent errors without continuous monitoring of debugging data. The proposed solution works for designs even in the absence of golden reference. The fifth contribution proposes to use artificial intelligence for post-silicon debugging. We presented a novel idea of using a recurrent neural network for debugging when a golden reference is present for training the network. Furthermore, the idea was also extended to designs where golden reference is not present