Danial Hooshyar

A flowchart-based intelligent tutoring system for improving problem-solving skills of novice programmers

Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem-solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutors actions in implementing a one-to-one personalized and adaptive teaching. In this paper, a novel Flowchart-based Intelligent Tutoring System FITS is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem-solving activities and learning computer programming. FITS not only takes full advantage of Bayesian networks, but also benefits from a multi-agent system using an automatic text-to-flowchart conversion approach for engaging novice programmers in flowchart development with the aim of improving their problem-solving skills. In the end, in order to investigate the efficacy of FITS in problem-solving ability acquisition, a quasi-experimental design was adopted by this research. According to the results, students in the FITS group experienced better improvement in their problem-solving abilities than those in the control group. Moreover, with regard to the improvement of a users problem-solving ability, FITS has shown to be considerably effective for students with different levels of prior knowledge, especially for those with a lower level of prior knowledge.

Mining biometric data to predict programmer expertise and task difficulty

Programming mistakes frequently waste software developers’ time and may lead to the introduction of bugs into their software, causing serious risks for their customers. Using the correlation between various software process metrics and defects, earlier work has traditionally attempted to spot such bug risks. However, this study departs from previous works in examining a more direct method of using psycho-physiological sensors data to detect the difficulty of program comprehension tasks and programmer level of expertise. By conducting a study with 38 expert and novice programmers, we investigated how well an electroencephalography and an eye-tracker can be utilized in predicting programmer expertise (novice/expert) and task difficulty (easy/difficult). Using data from both sensors, we could predict task difficulty and programmer level of expertise with 64.9 and 97.7% precision and 68.6 and 96.4% recall, respectively. The result shows it is possible to predict the perceived difficulty of a task and expertise level for developers using psycho-physiological sensors data. In addition, we found that while using single biometric sensor shows good results, the composition of both sensors lead to the best overall performance.

Comparing Programming Language Comprehension between Novice and Expert Programmers Using EEG Analysis

For programming language comprehension, high cognitive skills (e.g., reading, writing, working memory, etc.) and information processing are required. However, there are few papers that approach this from a neuroscientific perspective. In this paper, we examine program comprehension neuroscientifically and also observe the differences between novice and expert programmers. We designed an EEG (electroencephalogram) experiment and observed 18 participants during a series of program comprehension tasks. We found clear differences in program comprehension ability between novice and expert programmers. Experts exhibited higher brainwave activation than novices in electrodes F3 and P8. These results indicate that experts have outstanding program comprehension-associated abilities such as digit encoding, coarse coding, short-term memory, and subsequent memory effect. Our findings can serve as a foundation for future research in this pioneering field.

Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review

The most commonly used algorithm in recommendation systems is collaborative filtering. However, despite its wide use, the prediction accuracy of this algorithm is unexceptional. Furthermore, whether quantitative data such as product rating or purchase history reflect users’ actual taste is questionable. In this article, we propose a method to utilise user review data extracted with opinion mining for product recommendation systems. To evaluate the proposed method, we perform product recommendation test on Amazon product data, with and without the additional opinion mining result on Amazon purchase review data. The performances of these two variants are compared by means of precision, recall, true positive recommendation (TPR) and false positive recommendation (FPR). In this comparison, a large improvement in prediction accuracy was observed when the opinion mining data were taken into account. Based on these results, we answer two main questions: ‘Why is collaborative filtering algorithm not effective?’ and ‘Do quantitative data such as product rating or purchase history reflect users’ actual tastes?’

Data-Driven Approaches to Game Player Modeling: A Systematic Literature Review

Modeling and predicting player behavior is of the utmost importance in developing games. Experience has proven that, while theory-driven approaches are able to comprehend and justify a models choices, such models frequently fail to encompass necessary features because of a lack of insight of the model builders. In contrast, data-driven approaches rely much less on expertise, and thus offer certain potential advantages. Hence, this study conducts a systematic review of the extant research on data-driven approaches to game player modeling. To this end, we have assessed experimental studies of such approaches over a nine-year period, from 2008 to 2016; this survey yielded 46 research studies of significance. We found that these studies pertained to three main areas of focus concerning the uses of data-driven approaches in game player modeling. One research area involved the objectives of data-driven approaches in game player modeling: behavior modeling and goal recognition. Another concerned methods: classification, clustering, regression, and evolutionary algorithm. The third was comprised of the current challenges and promising research directions for data-driven approaches in game player modeling.

SITS: a solution-based intelligent tutoring system for students’ acquisition of problem-solving skills in computer programming

Abstract In learning systems and environment research, intelligent tutoring and personalisation are considered the two most important factors. An Intelligent Tutoring System can serve as an effective tool to improve problem-solving skills by simulating a human tutor’s actions in implementing one-to-one adaptive and personalised teaching. Thus, in this research, a solution-based intelligent tutoring system (SITS) is proposed. It benefits from Bayesian networks in managing uncertainty based on the probability theory for the process of decision-making so as to aid students learn computer programming. Additionally, SITS benefits from a multi-agent system that employs an automatic text-to-flowchart conversion approach to engage novice programmers in flowchart development with the aim of improving their problem-solving skills. Finally, the performance of SITS is investigated through an experimental study. It is revealed that SITS is not only capable of boosting students’ learning interest, attitude and technology acceptance, but it also helps students achieve more in terms of problem-solving activities.

A systematic review of data-driven approaches in player modeling of educational games

Recent years have seen growing interest in open-ended interactive educational tools such as games. One of the most crucial aspects of developing games lies in modeling and predicting individual behavior, the study of computational models of players in games. Although model-based approaches have been considered standard for this purpose, their application is often extremely difficult due to the huge space of actions that can be created by educational games. For this reason, data-driven approaches have shown promise, in part because they are not completely reliant on expert knowledge. This study seeks to systematically review the existing research on the use of data-driven approaches in player modeling of educational games. The primary objectives of this study are to identify, classify, and bring together the relevant approaches. We have carefully surveyed a 10-year sample (2008–2017) of research studies conducted on data-driven approaches in player modeling of educational games, and thereby found 67 significant research works. However, our criteria for inclusion reduced the sample to 21 studies that addressed four primary research questions, and so we analyzed and classified the questions, methods, and findings of these published works, which we evaluated and from which we drew conclusions based on non-statistical methods. We found that there are three primary avenues along which data-driven approaches have been studied in educational games research: first, the objective of data-driven approaches in player modeling of educational games, namely behavior modeling, goal recognition, and procedural content generation; second, approaches employed in such modeling; finally, current challenges of using data-driven approaches in player modeling of educational games, namely game data, temporal forecasting in player models, statistical techniques, algorithmic efficiency, knowledge engineering, problem of generalizability, and data sparsity problem. In conclusion we addressed four critical future challenges in the area, namely, the lack of proper and rich data publicly available to the researchers, the lack of a data-driven method to identify conceptual features from log data, hybrid player modeling approaches, and data mining techniques for individual prediction.

Flowchart-based Bayesian Intelligent Tutoring System for computer programming

There is a misconception of what programming is at the early stages of learning programming for Computer Science (CS) minors. More researches in this field have revealed that the lack of problem-solving skills, which is considered as one of the prominent shortcomings that novices deal with, is exacerbated by language syntax that the novices employ. A Flowchart-based Intelligent Tutoring System (FITS) is proposed in this research aimed at introducing the early stages of learning programming (CS1) to put the record straight. The students who have no prior knowledge of programming are the target audience of this research. In order to support novice programmers in beginning of programming, Bayesian network approach is applied mainly for decision making and to handle uncertainties in knowledge level of students. How to use Bayesian network to take full advantage of it as an inference engine for providing users with various guidance is described in this paper. Therefore, our proposed system provides users with dynamic guidance such as recommend learning goals, recommend options for flowchart development, and generate appropriate reading sequences. Additionally, our proposed systems architecture and its components are elaborated. Our future work is to evaluate the FITS by conducting an experimental study using novices.

A Procedural Content Generation-Based Framework for Educational Games: Toward a Tailored Data-Driven Game for Developing Early English Reading Skills:

Automated content generation for educational games has become an emerging research problem, as manual authoring is often time consuming and costly. In this article, we present a procedural content generation framework that intends to produce educational game content from the viewpoint of both designer and user. This framework generates content by means of genetic algorithm, and thereby offers designers the ability to control the process of content generation for various learning goals according to their preferences. It further takes into consideration how the content can adapt according to the skill of the users. We demonstrate effectiveness of the framework by way of an empirical study of human players in an educational language learning game aiming at developing early English reading skills of young children. The results of our study confirm that users’ performance measurably improves when game contents are customized to their individual ability, in contrast to their improvement in uncustomized games. Moreover, the results show that the lowest proficiency participants demonstrated greater improvements in performance while playing the customized game than did the more highly proficient participants.

Development and evaluation of a game-based bayesian intelligent tutoring system for teaching programming

Games with educational purposes usually follow a computer-assisted instruction concept that is predefined and rigid, offering no adaptability to each student. To overcome such problem, some ideas from Intelligent Tutoring Systems have been used in educational games such as teaching introductory programming. The objective of this study was to advance Online Game-based Bayesian Intelligent Tutoring System (OGITS) to enhance programming acquisition and online information searching skills, thus improving students’ ability in web-based problem solving through board games. The study sample comprised 79 college students in introductory level Computer Science classes. Qualitative and quantitative data were then gathered. Results of this study revealed generally favorable opinions about OGITS. As OGITS targets individual knowledge acquisition of computer programming and web-based problem-solving skills, it offers a suitable learning environment for students both as a stand-alone course and as a supplement to traditional classroom settings.