Tutoring System For Programming Research Articles

Evaluating the user’s experience, adaptivity and learning outcomes of a fuzzy-based intelligent tutoring system for computer programming for academic students in Greece

Nowadays, the improvement of digital learning with Artificial Intelligence has attracted a lot of research, as it provides solutions for individualized education styles which are independent of place and time. This is particularly the case for computer science, as a tutoring domain, which is rapidly growing and changing and as such, learners need frequent update courses. In this paper, we present a thorough evaluation of a fuzzy-based intelligent tutoring system (ITS), that teaches computer programming. The evaluation concerns multiple aspects of the ITS. The evaluation criteria are: (i) context, (ii) effectiveness, (iii) efficiency, (iv) accuracy, (v) usability and satisfaction, and (vi) engagement and motivation. In the evaluation process students of an undergraduate program in Informatics of the University of Piraeus in Greece participated. The evaluation method that was used included questionnaires, analysis of log files and experiments. Also, t-tests were conducted to certify the validity of the evaluation results. Indeed, the evaluation results are very positive and show that the incorporated fuzzy mechanism to the presented ITS enhances the system with Artificial Intelligence and through this, it increases the learners’ satisfaction and new knowledge learning and mastering, improves the recommendation accuracy of the system, the efficacy of interactions, and contributes positively to the learners’ engagement in the learning process.

Adaptive Assessment and Content Recommendation in Online Programming Courses: On the Use of Elo-rating

Online learning systems should support students preparedness for professional practice by equipping them with the necessary skills while keeping them engaged and active. In that regard, the development of online learning systems that support students’ development and engagement with programming is a challenging process. Early career computer science professionals are required not only to understand and master numerous programming concepts but also to efficiently learn how to apply them in different contexts. A prerequisite for an effective and engaging learning process is the existence of adaptive and flexible learning environments that are beneficial for both students and teachers. Students can benefit from personalized content adapted to their individual goals, knowledge, and needs; while teachers can be relieved from the pressure to uniformly and promptly evaluate hundreds of student assignments. This study proposes and puts into practice a method for evaluating learning content difficulty and students’ knowledge proficiency utilizing a modified Elo-rating method. The proposed method effectively pairs learning content difficulty with students’ proficiency, and creates personalized recommendations based on the generated ratings. The method was implemented in a programming tutoring system and tested with interactive learning content for object oriented-programming. By collecting quantitative and qualitative data from students who used the system for one semester, the findings reveal that the proposed method can generate recommendations that are relevant to students and has the potential to assist teachers in grading students by providing a more holistic understanding of their progress over time.

Learning in smart environments: user-centered design and analytics of an adaptive learning system

The complexity of today’s learning processes and practices entails various challenges. It is becoming much harder for teachers to observe, control, and adjust the learning process. Moreover, contemporary teaching is enhanced with different technologies and systems that not only support information-transfer, but also make this process more effective. In this paper we present the Programming Tutoring System (ProTuS), which provides smart and interactive content, personalization options, adaptive features, and learning analytics as a support for users engaged in learning complex cognitive skills. Our contribution in this paper is twofold, conceptual and empirical. The paper presents the interactive learning analytics component developed in ProTuS and the results from the empirical study. The study shows that students find adaptive learning systems to be useful in monitoring progress, promoting reflective practices, and receiving feedback to better understand their actions and learning strategies.

Intelligent Tutor for Programming System Using Multiple Intelligences

In this article the difficulties present it teaching object-oriented programming and Intelligent Tutoring Systems are exposed. the usefulness of Intelligent Tutoring Systems taking into account technical data mining, among which include artificial neural networks is based. The author explains in the article the proposal of a model, using data mining techniques to extract and analyze information from student interactions with the platform in the process of teaching and learning. To achieve this goal are considered the predominant types of intelligence in each, as will achieve more individualized solutions. The results show a positive impact in solving problems, improving the learning process.

Motivation Assessment Model Using Fuzzy Logic in Programming Tutoring System

Motivation is a key success factor in learning programming. However, motivation aspect has not been fully addressed in Programming Tutoring System. This paper proposed a Motivation Assessment Model for Programming Tutoring System. The aim is to detect and measure students’ motivation level so that the tutoring system can deliver the tutorial materials accordingly, much like a human tutor does. In this study, various motivation factors, variables and techniques for measuring students’ motivation in tutoring system were investigated. Based on self-efficacy theory, Effort, Choice of Activities, Performance and Persistence were proposed as motivation factors. Parameter(s) for each factor and Fuzzy Logic as a prediction technique were also discussed. As for future work, the proposed model will be implemented in Java platform and tested on programming students using Moodle. Keywords: Fuzzy Logic, Motivation, Motivation Assessment Model, Programming Tutoring System, Self-Efficacy Section

Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

Personalization of the e-learning systems according to the learner’s needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tag-based recommendations has been used and evaluated in a programming tutoring system.

Programming Tutoring Systems and Motivation Assessment Model

Writing computer programs is a challenging feat for most undergraduate students although it is one of the core subjects that they are expected to master. Due to the rapid increase in the use of computers and Internet in education a large number of Programming Tutoring Systems have been developed to reduce the obstacles to programming. Motivation has been shown to be one of the most important issues for student success in learning. Yet, most of these Programming Tutoring Systems do not take into consideration motivation as a factor in their system. Hence more research is needed in this area. This paper presents a review of previous research on motivation assessment models in eLearning environment. These models can be integrated into Programming Tutoring Systems to assess student motivation in learning programming.

Web Based E-Learning System for C-Language

This paper introduces a Web-based tutoring system for computer programming that provides an integrated environment for web programming. The proposed system is web-based. It provides an online editor for writing, editing, updating, and executing programming code, so the students can execute examples and practical exercises online, it can help a student navigate through the online course materials, recommend learning goals, and generate appropriate reading sequences.. In addition, it provides auto complete and syntax highlighting in editing code, which could help students to remember the syntax more easily. The proposed system provides communication and interactive forum among students and teachers. And it provides the teachers with an online exam system. It monitors, collects and keeps information about students performance and activities. Therefore, teachers can use the recorded information to keep tracking of the students progress.

Ontology-based architecture with recommendation strategy in java tutoring system

The aim of Semantic Web is to provide distributed information with well-defined meaning, understandable for humans as well as machines. E-learning is an important domain which can be benefited from the Semantic Web technology. Ontologies, as a building structure of Semantic Web, will fundamentally change the way in which e-learning systems are constructed. The explicit conceptualization of system components in a form of ontology facilitates knowledge sharing, knowledge reuse, communication and collaboration among system components, and construction of intensive and expressive systems. In previous research, we implemented tutoring system named Protus (PRogramming TUtoring System) that is used for learning basic concepts of Java programming language. Protus uses principles of learner style identification and content recommendation for course personalization. The new version of the system called Protus 2.0, supported by several ontologies, as well as examples of its usage for performing personalization are presented in this paper. Architecture of new system extends the usage of Semantic Web concepts, where the representation of each Protus 2.0 component is made by a specific ontology, making possible a clear separation of the tutoring system components and explicit communication among them.

Protus 2.0: Ontology-based semantic recommendation in programming tutoring system

With the development of the Semantic web the use of ontologies as a formalism to describe knowledge and information in a way that can be shared on the web is becoming common. The explicit conceptualization of system components in a form of ontology facilitates knowledge sharing, knowledge reuse, communication and collaboration and construction of knowledge rich and intensive systems. Semantic web provides huge potential and opportunities for developing the next generation of e-learning systems. In previous work, we presented tutoring system named Protus (PRogramming TUtoring System) that is used for learning the essence of Java programming language. It uses principles of learning style identification and content recommendation for course personalization. This paper presents new approach to perform effective personalization highly based on Semantic web technologies performed in new version of the system, named Protus 2.0. This comprises the use of an ontology and adaptation rules for knowledge representation and inference engines for reasoning. Functionality, structure and implementation of a Protus 2.0 ontology as well as syntax of SWRL rules implemented for on-the-fly personalization will be presented in this paper.

Human Computer Interaction Design of the LP-ITS: Linear Programming Intelligent Tutoring Systems

The Linear Programming Intelligent Tutoring System (LP-ITS) was designed and developed in an effort to help students who are facing problems in linear programming. This tutoring system is unique in several ways. Most programming tutoring systems require the instructor to creator problems with corresponding solutions. LP-ITS, on the other hand, generate the problem and solution automatically. LP-ITS analyzes the student’s solution step by step to determine the weak points of the student and intelligently guides the student towards the correct solution of the linear programming problem. LP-ITS is intended to be used by students enrolled in Linear Programming course at the University level. This paper discusses the Human Computer Interaction (HCI) design elements in the LP-ITS for improving the Linear programming Intelligent Tutoring System. LP-ITS has been and is currently being field-tested by students enrolled in Operations Research in the Faculty of Engineering and Information Technology at Al-Azhar University in Gaza.

E-Learning personalization based on hybrid recommendation strategy and learning style identification

Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a programming tutoring system - Protus, which can automatically adapt to the interests and knowledge levels of learners. This system recognizes different patterns of learning style and learners’ habits through testing the learning styles of learners and mining their server logs. Firstly, it processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the learners through mining the frequent sequences by the AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content according to the ratings of these frequent sequences, provided by the Protus system. Some experiments were carried out with two real groups of learners: the experimental and the control group. Learners of the control group learned in a normal way and did not receive any recommendation or guidance through the course, while the students of the experimental group were required to use the Protus system. The results show suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences.

A web-based bayesian intelligent tutoring system for computer programming

In this paper, we present a Web-based intelligent tutoring system, called BITS. The decision making process conducted in our intelligent system is guided by a Bayesian network approach to support students in learning computer programming. Our system takes full advantage of Bayesian networks, which are a formal framework for uncertainty management in Artificial Intelligence based on probability theory. We discuss how to employ Bayesian networks as an inference engine to guide the students' learning processes. In addition, we describe the architecture of BITS and the role of each module in the system. Whereas many tutoring systems are static HTML Web pages of a class textbook or lecture notes, our intelligent system can help a student navigate through the online course materials, recommend learning goals, and generate appropriate reading sequences.

Ludwig

An online programming tutoring and assessment system is described, and the results of a preliminary study are presented. Ten students in an introductory C++ programming course used the system; both qualitative and quantitative data were collected and suggest that a future large-scale implementation will yield beneficial results

Transformation-based diagnosis of student programs for programming tutoring systems

A robust technology that automates the diagnosis of students' programs is essential for programming tutoring systems. Such technology should be able to determine whether programs coded by a student are correct. If a student's program is incorrect, the system should be able to pinpoint errors in the program as well as explain and correct the errors. Due to the difficulty of this problem, no existing system performs this task entirely satisfactorily, and this problem still hampers the development of programming tutoring systems. This paper describes a transformation-based approach to automate the diagnosis of students' programs for programming tutoring systems. Improved control-flow analysis and data-flow analysis are used in program analysis. Automatic diagnosis of student programs is achieved by comparing the student program with a specimen program at the semantic level after both are standardized. The approach was implemented and tested on 525 real student programs for nine different programming tasks. Test results show that the method satisfies the requirements stated above. Compared to other existing approaches to automatic diagnosis of student programs, the approach developed here is more rigorous and safer in identifying student programming errors. It is also simpler to make use of in practice. Only specimen programs are needed for the diagnosis of student programs. The techniques of program standardization and program comparison developed here may also be useful for research in the fields of program understanding and software maintenance.

SFG-GP: An effective tutoring system for goal programming

Most educational software packages for management science/operations research (MS/OR) introduced in the 1980s were designed mainly to obtain model solutions efficiently. As an effective teaching (or learning) aid, MS/OR software should be designed as a drill and practice program to encourage the student's participation in solving a model. It should also provide clear explanations of the solution procedure. Consequently, educational MS/OR software should adopt a solution algorithm that can help students learn MS/OR models more effectively, rather than simply achieving computational efficiency. Thus, MS/OR software should ask questions, compare answers of the students and experts, and provide explanations to enhance students' understanding of MS/OR techniques. To demonstrate these features, we developed SFG-GP (Signal Flow Graph Method of Goal Programming) which not only encourages students to participate in solution procedures, but also provides necessary explanations using predetermined sentences.

Multiple multimodal mentors: delivering computer-based instruction via specialized anthropomorphic advisors

Abstract We describe the Smalltalk Gurus, components of the MoleHill intelligent tutoring system for Smalltalk programming. The Gurus offer help on plans for achieving goals in the Smalltalk environment, as well as remediation for students' incorrect and less-than-optimal plans. The Gurus' assistance is provided via the multimodal media of animation and voice-over audio. MoleHill employs multiple Gurus to deliver advice and instruction concerning disparate information domains, thus facilitating learners' cognitive organization and assimilation of new knowledge and information. We have labelled the approach instantiated by the Smalltalk Gurus the guru instructional model, one which is generally applicable to computer-based advisory systems.

Towards an intelligent tutoring system for Pascal programming

Expert systems have been extensively used in education and training to represent or reason about subject matter, students or trainees, teaching or training strategies. The Pascal Debugging Aid (PDA) is an expert system designed to assist students while debugging Pascal programs. Here we describe the development process for PDA, identify its shortcomings as a tutoring system and suggest how PDA can be endowed with intelligent tutoring abilities.