University students' achievement goals and help-seeking strategies in an intelligent tutoring system

Help seeking behavior in an intelligent tutoring system was analyzed to identify help seeking strategies, and it was investigated whether the use of these strategies could be predicted by achievement goal scores. Discrete Markov Models and a k-means clustering algorithm were used to identify strategies, and logistic regression analyses (n = 45) were used to analyze the relation between achievement goals and strategy use. Five strategies were identified, three of which were predicted by achievement goal scores. These strategies were labeled Little Help, Click Through Help, Direct Solution, Step By Step, and Quick Solution. The Click Through Help strategy was predicted by mastery avoidance goals, the Direct Solution strategy was negatively predicted by mastery avoidance goals and positively predicted by performance avoidance goals, and the Quick Solution strategy was negatively predicted by performance approach goals.     

User modelling and user adapted interaction in an intelligent tutoring system

User modelling and user-adapted interaction are crucial to the provision of true individualised instruction, which intelligent tutoring systems strive to achieve. This paper presents how user (student) modelling and student adapted instruction is achieved in FITS, an intelligent tutoring system for the fractions domain. Some researchers have begun questioning both the need for detailed student models as well as the pragmatic possibility of building them. The key contributions of this paper are in its attempt to rehabilitate student modelling/adaptive tutoring within ITSs and in FITS's practical use of simple techniques to realise them with seemingly encouraging results; some illustrations are given to demonstrate the latter.     

Student modeling and assessment in intelligent tutoring of software patterns

This paper presents the design, implementation, and evaluation of a student model in DEPTHS (Design Pattern Teaching Help System), an intelligent tutoring system for learning software design patterns. There are many approaches and technologies for student modeling, but choosing the right one depends on intended functionalities of an intelligent system that the student model is going to be used in. Those functionalities often determine the kinds of information that the student model should contain. The student model used in DEPTHS is a result of combining two widely known modeling approaches, namely, stereotype and overlay modeling. The model is domain independent and can be easily applied in other learning domains as well. To keep student model update during the learning process, DEPTHS makes use of a knowledge assessment method based on fuzzy rules (i.e., a combination of production rules and fuzzy logics). The evaluation of DEPTHS performed with the aim of assessing the system’s overall effectiveness and the accuracy of its student model, indicated several advantages of the DEPTHS system over the traditional approach to learning design patterns, and encouraged us to move on further with this research.     

An adaptation algorithm for an intelligent natural language tutoring system

The focus of computerised learning has shifted from content delivery towards personalised online learning with Intelligent Tutoring Systems (ITS). Oscar Conversational ITS (CITS) is a sophisticated ITS that uses a natural language interface to enable learners to construct their own knowledge through discussion. Oscar CITS aims to mimic a human tutor by dynamically detecting and adapting to an individual's learning styles whilst directing the conversational tutorial. Oscar CITS is currently live and being successfully used to support learning by university students. The major contribution of this paper is the development of the novel Oscar CITS adaptation algorithm and its application to the Felder–Silverman learning styles model. The generic Oscar CITS adaptation algorithm uniquely combines the strength of an individual's learning style preference with the available adaptive tutoring material for each tutorial question to decide the best fitting adaptation. A case study is described, where Oscar CITS is implemented to deliver an adaptive SQL tutorial. Two experiments are reported which empirically test the Oscar CITS adaptation algorithm with students in a real teaching/learning environment. The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial.     

A problem solving oriented intelligent tutoring system to improve students' acquisition of basic computer skills

Personalization and intelligent tutor are two key factors in the research on learning environment. Intelligent tutoring system (ITS), which can imitate the human teachers' actions to implement one-to-one personalized teaching to some extent, is an effective tool for training the ability of problem solving. This research firstly discusses the concepts and methods of designing problem solving oriented ITS, and then develops the current iTutor based on the extended model of ITS. At last, the research adopts a quasi-experimental design to investigate the effectiveness of iTutor in skills acquisition. The results indicate that students in iTutor group experience better learning effectiveness than those in the control group. iTutor is found to be effective in improving the learning effectiveness of students with low-level prior knowledge.     

Development of a reading material recommendation system based on a knowledge engineering approach

In a language curriculum, the training of reading ability is one of the most important aspects. Previous studies have shown the importance of assigning proper articles to individual students for training their reading ability; nevertheless, previous experience has also shown the challenges of this issue owing to the complexity of personal factors as well as the diverse properties of the candidate articles to be taken into consideration. This study proposes a knowledge engineering approach for developing reading material recommendation systems by eliciting domain knowledge from multiple experts. Experimental results on 29 senior high school students show that the developed system is able to provide expert-like recommendations to the students by taking preferences and knowledge levels of individual students as well as categories and traits of articles into consideration.     

An adaptive and innovative question-driven competition-based intelligent tutoring system for learning

ISCARE (Information System for Competition based on pRoblem solving in Education) is a new and innovative intelligent tutoring system that we have designed and implemented. This tool allows the competition among students for improving their learning process in a course. The tool takes some ideas from the Swiss-system widely used in chess and adapts them to the educational area. The competition is based on different tournaments and rounds. In each round, students are assigned in pairs of two, which compete one against another, and each pair receives different questions that students have to solve in a limit of time. Students can see their partial ratings after each round and their final rating after a tournament. A lot of knowledge from different disciplines was used to design, and implement this system, as ISCARE includes different functionality such as the students’ registration into the system, the creation of tournaments, the registration and assignment of students to tournaments, the management of each tournament life cycle (started, in execution, finished, etc.), the addition of the different exercises to tournaments, the calculation of pairs of students for each round with different algorithms, the assignment of exercises per round and pair, the scorings of the students per round and tournament, the management of the students’ ratings, or the visualization of information. This paper presents the ISCARE intelligent tutoring system, describing its different options, menus, or functionality as well as its architecture and the specific modeling to achieve the desired features.     

Using background knowledge in case-based legal reasoning: A computational model and an intelligent learning environment

Researchers in the field of AI and Law have developed a number of computational models of the arguments that skilled attorneys make based on past cases. However, these models have not accounted for the ways that attorneys use middle-level normative background knowledge (1) to organize multi-case arguments, (2) to reason about the significance of differences between cases, and (3) to assess the relevance of precedent cases to a given problem situation. We present a novel model, that accounts for these argumentation phenomena. An evaluation study showed that arguments about the significance of distinctions based on this model help predict the outcome of cases in the area of trade secrets law, confirming the quality of these arguments. The model forms the basis of an intelligent learning environment called CATO, which was designed to help beginning law students acquire basic argumentation skills. CATO uses the model for a number of purposes, including the dynamic generation of argumentation examples. In a second evaluation study, carried out in the context of an actual legal writing course, we compared instruction with CATO against the best traditional legal writing instruction. The results indicate that CATO's example-based instructional approach is effective in teaching basic argumentation skills. However, a more “integrated” approach appears to be needed if students are to achieve better transfer of these skills to more complex contexts. CATO's argumentation model and instructional environment are a contribution to the research fields of AI and Law, Case-Based Reasoning, and AI and Education.     

Supporting skill integration in an intelligent tutoring system for code tracing

Background

Skill integration is vital in students' mastery development and is especially prominent in developing code tracing skills which are foundational to programming, an increasingly important area in the current STEM education. However, instructional design to support skill integration in learning technologies has been limited.

Objectives

The current work presents the development and empirical evaluation of instructional design targeting students' difficulties in code tracing particularly in integrating component skills in the Trace Table Tutor (T3), an intelligent tutoring system.

Methods

Beyond the instructional features of active learning, step-level support, and individualized problem selection of intelligent tutoring systems (ITS), the instructional design of T3 (e.g., hints, problem types, problem selection) was optimized to target skill integration based on a domain model where integrative skills were represented as combinations of component skills. We conducted an experimental study in a university-level introductory Python programming course and obtained three findings.

Results and Conclusions

First, the instructional features of the ITS technology support effective learning of code tracing, as evidenced by significant learning gains (medium-to-large effect sizes). Second, performance data supports the existence of integrative skills beyond component skills. Third, an instructional design focused on integrative skills yields learning benefits beyond a design without such focus, such as improving performance efficiency (medium-to-large effect sizes).

Major Takeaways

Our work demonstrates the value of designing for skill integration in learning technologies and the effectiveness of the ITS technology for computing education, as well as provides general implications for designing learning technologies to foster robust learning.     

The role of probability-based inference in an intelligent tutoring system

Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring systems (ITSs). Basic concepts of the approach are briefly reviewed, but the emphasis is on the considerations that arise when one attempts to operationalize the abstract framework of probability-based reasoning in a practical ITS context. The discussion revolves around HYDRIVE, an ITS for learning to troubleshoot an aircraft hydraulics system. HYDRIVE supports generalized claims about aspects of student proficiency through probabilitybased combination of rule-based evaluations of specific actions. The paper highlights the interplay among inferential issues, the psychology of learning in the domain, and the instructional approach upon which the ITS is based.     

The development of a hybrid intelligent system for developing marketing strategy

In this paper, the development of a hybrid intelligent system for developing marketing strategy is described. The hybrid system has been developed to: provide a logical process for strategic analysis; support group assessment of strategic marketing factors; help the coupling of strategic analysis with managerial intuition and judgement; help managers deal with uncertainty and fuzziness; and produce intelligent advice on setting marketing strategy. In this system, the strengths of expert systems, fuzzy logic and artificial neural networks (ANNs) are combined to support the process of marketing strategy development. Moreover, the advantages of Porter's five forces model and the directional policy matrices (DPM) are also integrated to assist strategic analysis. In the paper, the software architecture of the hybrid system is discussed in details. Particularly, the group assessment support module, the fuzzification of strategic factors, and the fuzzy reasoning for setting marketing strategy are addressed. In addition, the empirical field work on evaluating the hybrid system is also summarised. The empirical evidence indicates that the hybrid intelligent system is helpful and useful in supporting the development of marketing strategy.     

Combined inner and outer loop feedback in an intelligent tutoring system for statistics in higher education

Intelligent tutoring systems (ITSs) can provide inner loop feedback about steps within tasks, and outer loop feedback about performance on multiple tasks. While research typically addresses these feedback types separately, many ITSs offer them simultaneously. This study evaluates the effects of providing combined inner and outer loop feedback on social sciences students' learning process and performance in a first-year university statistics course. In a 2 x 2 factorial design (elaborate inner loop vs. minimal inner loop and outer loop vs. no outer loop feedback) with 521 participants, the effects of both feedback types and their combination were assessed through multiple linear regression models. Results showed mixed effects, depending on students' prior knowledge and experience, and no overall effects on course performance. Students tended to use outer loop feedback less when also receiving elaborate inner loop feedback. We therefore recommend introducing feedback types one by one and offering them for substantial periods of time.     

MRM: A matrix representation and mapping approach for knowledge acquisition

Knowledge acquisition plays a critical role in constructing a knowledge-based system (KBS). It is the most time-consuming phase and has been recognized as the bottleneck of KBS development. This paper presents a matrix representation and mapping (MRM) approach to facilitate the effectiveness of knowledge acquisition in building a KBS. The proposed MRM approach, which is based on matrix representation and mapping operations, comprises six consecutive steps for generating rules. The procedure in each step is elaborated. A case study on primarily diagnosing an automotive system is employed to illustrate how the MRM approach works.     

Evaluating the persona effect of an interface agent in a tutoring system

Abstract This paper describes the evaluation of the persona effect of a speech-driven anthropomorphic agent that has been embodied in the interface of an intelligent tutoring system (ITS). This agent is responsible for guiding the student in the environment and communicating the system's feedback messages. The agent was evaluated in terms of the effect that it could have on students' learning, behaviour and experience. The participants in the experiment were divided into two groups: half of them worked with a version of the ITS which embodied the agent and the rest worked with an agent-less version. The results from this study confirm the hypothesis that a pedagogical agent incorporated in an ITS can enhance students' learning experience. On the other hand, the hypothesis that the presence of the agent improves short-term learning effects was rejected.     

A personalized summarization of video life-logs from an indoor multi-camera system using a fuzzy rule-based system with domain knowledge

Information summarization and retrieval are significant research topics associated with recent advancements in sensor devices, data compression and storage techniques, and high-speed internet. As a result of these advances, it is possible for people to collect huge life-logs. Video is one of the most important life information sources. This paper describes a method of summarizing video life-logs in an office environment with a multi-camera system. Previously, multi-camera systems have been used to track moving objects or to cover a wide area. This paper focuses on capturing diverse views of each office event using a multi-camera system with several cameras observing the same area. The summarization process includes camera view selection and event sequence summarization. View selection produces a single event sequence from multiple event sequences by selecting an optimal view at each time, for which domain knowledge based on the elements of the office environment and rules from questionnaire surveys have been used. Summarization creates a summary sequence from whole sequences by using a fuzzy rule-based system to approximate human decision making. The user-entered degrees of interest in objects, persons, and events are used for a personalized summarization. We confirmed experimentally that the proposed method provides promising results.     

Stability and change in adolescents’ task-specific achievement goals and implications for learning mathematics with intelligent tutors

Individuals’ achievement goals are known to influence learning behaviors and academic achievement. However, prior research also indicates that undergraduates’ achievement goals for psychology coursework vary from assignment to assignment. The effect of stability of achievement goals on learning behaviors and outcomes has yet to be explored. This study examined how adolescents’ achievement goals varied over mathematics units completed in an intelligent tutoring system, and whether strength or variability in achievement goals influenced behavior or achievement. At the group level, achievement goals correlated significantly from unit to unit; mean scores were not significantly different over time. However, individuals’ goal scores changed reliably across units. No relationships were found between the strength of students’ achievement goal scores and learning behaviors or performance. However, students with stable mastery approach goals achieved better grades than those with more variable mastery-approach goals. Students with stable performance-approach goals engaged in fewer help-seeking behaviors than those with variable performance approach goals.     

Complementary machine intelligence and human intelligence in virtual teaching assistant for tutoring program tracing

This study proposes a virtual teaching assistant (VTA) to share teacher tutoring tasks in helping students practice program tracing and proposes two mechanisms of complementing machine intelligence and human intelligence to develop the VTA. The first mechanism applies machine intelligence to extend human intelligence (teacher answers) to evaluate the correctness of student program tracing answers, to locate student errors, and to generate hints to indicate errors. The second mechanism applies machine intelligence to reuse human intelligence (previous hints that the teacher gave to other students in a similar error situation) to provide program-specific hints. Two evaluations were conducted with 85 and 64 participants, respectively. The evaluation results showed that the system helped above 89% of students correct their errors. The error-indicating hints generated by the first mechanism help students correct more than half of errors. Each teacher-generated hint was reused averagely three times by the second mechanism. The results also revealed that some error situations occurred frequently and occupied a major occurred percentage of student error situations. In sum, the VTA and these two mechanisms reduce teacher tutoring load and reduce the complexity of developing machine intelligence.     

Developing a distributed knowledge model for knowledge management in collaborative development and implementation of an enterprise system

Recently, enterprise systems have been extensively adopted to boost enterprise competitiveness. The development and implementation of enterprise systems is a knowledge intensive procedure, being related to enterprise processes and involving information, system and software engineering technologies. Consequently, knowledge management is required to enhance the effectiveness of enterprise system development and implementation, thus helping to increase industrial competitiveness.This study aims to develop a distributed knowledge model for knowledge management, capable of supporting the collaborative development and implementation of enterprise systems. This objective can be obtained by performing the following tasks: (1) modeling and characterization of the collaborative development and implementation process, (2) identification, analysis and modeling of involved knowledge, and (3) development of a distributed knowledge model for knowledge management related to the collaborative development and implementation of enterprise systems.     

A hybrid intelligent system for PID controller using in a steel rolling process

With the aim to improve the steel rolling process performance, this research presents a novel hybrid system for selecting the best parameters for tuning in open loop a PID controller. The novel hybrid system combines rule based system and Artificial Neural Networks. With the rule based system, it is modeled the existing knowledge of the PID controller tuning in open loop and, with Artificial Neural Network, it is completed the rule based model that allow to choose the optimal parameters for the controller. This hybrid model is tested with a long dataset to obtain the best fitness. Finally, the novel research is validated on a real steeling roll process applying the hybrid model to tune a PID controller which set the input speed in each of the gearboxes of the process.