Rule-based expert systems to support step-by-step guidance in algebraic problem solving: The case of the tutor PAT2Math

In order for an Intelligent Tutoring System (ITS) to correct students’ exercises, it must know how to solve the same type of problems that students do and the related knowledge components. It can, thereby, compare the desirable solution with the student’s answer. This task can be accomplished by an expert system. However, it has some drawbacks, such as an exponential complexity time, which impairs the desirable real-time response. In this paper we describe the expert system (ES) module of an Algebra ITS, called PAT2Math. The ES is responsible for correcting student steps and modeling student knowledge components during equations problem solving. Another important function of this module is to demonstrate to students how to solve a problem. In this paper, we focus mainly on the implementation of this module as a rule-based expert system. We also describe how we reduced the complexity of this module from O(n^d) to O(d), where n is the number of rules in the knowledge base, by implementing some meta-rules that aim at inferring the operations students applied in order to produce a step. We evaluated our approach through a user study with forty-three seventh grade students. The students who interacted with our tool showed statistically higher scores on equation solving tests, after solving algebra exercises with PAT2Math during an approximately two-hour session, than students who solved the same exercises using only paper and pencil.     

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.     

The development of an ESL OWL, or learning how to tutor writing online

This essay describes the development of an ESL OWL by grounding practices in language and literacy pedagogy theory. An initial discussion explores OWLs emulating physical writing center spaces. Two areas of concern are then addressed in meeting the needs of second language writers as they relate to practices and training for online tutoring: error correction—an area of frequent concern to second language writers—and increased interactivity—meeting second language writer expectations and creating autonomous learners. Issues of plagiarism by second language writers are discussed as related to the type of feedback OWL tutors can provide. Highlighted throughout are samples of interactions between tutors and writers that show a process of learning how to create dialogue rather than dictations from the tutor to clean up a single essay.     

eTeacher: Providing personalized assistance to e-learning students

In this paper we present eTeacher, an intelligent agent that provides personalized assistance to e-learning students. eTeacher observes a student’s behavior while he/she is taking online courses and automatically builds the student’s profile. This profile comprises the student’s learning style and information about the student’s performance, such as exercises done, topics studied, exam results. In our approach, a student’s learning style is automatically detected from the student’s actions in an e-learning system using Bayesian networks. Then, eTeacher uses the information contained in the student profile to proactively assist the student by suggesting him/her personalized courses of action that will help him/her during the learning process. eTeacher has been evaluated when assisting System Engineering students and the results obtained thus far are promising.     

Computer-based tutoring of visual concepts: from novice to expert

Abstract Computer-based tutoring of visual concepts is a largely unexplored area. The ability to classify images, to identify features and to describe abnormalities is a necessary part of training in many professions, such as radiology, anatomy, botany and geology. To a lesser level it is important for developing everyday skills such as identifying birds, trees, rocks, and flowers. The aim of this paper is to show how computers might be used in visual concept teaching, to store and display large stocks of exemplar images, to carry out a tutorial dialogue with the learner, and to provide learning aids such as concept maps, feature spaces, and structural models. The paper describes well-established methods for teaching visual concepts and indicates how computers can supplement the human teacher in tutoring students with differing levels of expertise.     

Artificial intelligence approach to support statistical quality control teaching

Statistical quality control – SQC (consisting of Statistical Process Control, Process Capability Studies, Acceptance Sampling and Design of Experiments) is a very important tool to obtain, maintain and improve the Quality level of goods and services produced by an organization. Despite its importance, and the fact that it is taught in technical and college courses, as well as in companies’ training sectors, SQC has been largely misused. An inappropriate teaching approach may be the cause of such problem; therefore it has motivated the development of a model for SQC teaching, allowing its learners to correctly apply SQC techniques. After a survey regarding the concept needed to correctly apply SQC, its use and teaching/training methods, the model’s contents and methodology were defined. We also realized the opportunity of incorporating a computer environment for the model, permitting the practice of the needed SQC concepts and skills. An Artificial Intelligence approach was used to develop the computer environment, resulting in an Intelligent Tutoring System, the STCEQ. The paper discusses the main characteristics of the system, its functioning, benefits of using such a system and the results we obtained while using this system.     

Identifying students’ mathematical skills from a multiple-choice diagnostic test using an iterative technique to minimise false positives

There is anecdotal evidence that a significant number of students studying computing related courses at degree level have difficulty with sub-GCE mathematics. Testing of students’ skills is often performed using diagnostic tests and a number of computer-based diagnostic tests exist, which work, essentially, by testing one specific diagnostic skill at a time.     

Task results processing for the needs of task-oriented design environments

This paper presents learners’ task results gathered by means of an example task-oriented environment for knowledge testing and processed by EXCEL. The processing is domain- and task-independent and includes automatic calculation of several important task and session’s parameters, drawing specific graphics, generating tables, and analyzing the correlation coefficient. The intention is to design and implement a specialized tool called postprocessor for support of a common task base, learner’s model and decision making of the author and teacher by means of an environment for individually planned teaching courseware.     

Conceptual indexing and active retrieval of video for interactive learning environments

Selecting an instructive story from a video case base is an information retrieval problem, but standard indexing and retrieval techniques [1] were not developed with such applications in mind. The classical model assumes a passive retrieval system queried by interested and well-informed users. In educational situations, students cannot be expected to form appropriate queries or to identify their own ignorance. Systems that teach must, therefore, be active retrievers that formulate their own retrieval cues and reason about the appropriateness of intervention.

The Story Producer for InteractivE Learning (SPIEL) is an active retrieval system for recalling stories to tell to students who are learning social skills in a simulated environment [2, 3]. SPIEL is a component of the Guided Social Simulation (GuSS) architecture [4] used to build YELLO, a program that teaches account executives the fine points of selling Yellow Pages advertising. SPIEL uses structured, conceptual indices derived from research in case-based reasoning [5, 6]. SPIEL's manually-created indices are detailed representations of what stories are about, and they are needed to make precise assessments of stories' relevance.

SPIEL's opportunistic retrieval architecture operates in two phases. During the storage phase, the system uses its educational knowledge encapsulated in a library of “storytelling strategies” to determine, for each story, what an opportunity to tell that story would look like. During the retrieval phase, the system tries to recognize those opportunities while the student interacts with the simulation. This design is similar to “opportunistic memory” architectures proposed for opportunistic planning [7, 8].     

Using a hybrid rule-based approach in developing an intelligent tutoring system with knowledge acquisition and update capabilities

In this paper, we present the architecture and describe the functionality of an Intelligent Tutoring System (ITS), which uses an expert system to make decisions during the teaching process. The expert system uses neurules for knowledge representation of the pedagogical knowledge. Neurules are a type of hybrid rules integrating symbolic rules with neurocomputing. The expert system consists of three components: the user modelling unit, the pedagogical unit and the inference system. The pedagogical knowledge is distributed in a number of neurule bases within the user modelling and the pedagogical unit. Another important component of the ITS, for both its development and maintenance, is its knowledge management unit, which provides knowledge acquisition and knowledge update capabilities to the system, that is, offers expert knowledge authoring capabilities to the system.     

Using Co-Lab to build System Dynamics models: Students’ actions and on-line tutorial advice

Modeling offers a promising form of constructivist learning for students. By making and executing models of dynamic systems in a computer environment, students are stimulated to learn about the specific domain that is modeled as well as about the process of modeling in general. However, learning by modeling also leads to characteristic student mistakes, based on a combination of faulty domain knowledge and insufficient modeling skills. In this article, we describe a method of generating advice to students during their modeling process. The on-line advice system was informed by our observations of a teacher who gave advice via a textual communication tool to students building models with a System Dynamics model editor. The first version of the on-line advice system was evaluated in two ways: first, three teachers evaluated the advice the system generated for students’ final solutions; second, we analyzed the advice the system provided as it was used by a sample of students who were building a physics model. These evaluations showed that the overall approach, including matching a student solution to a family of reference solutions together with the other mechanisms of the advice system, is valid. However, they also highlighted the difficulty of building ‘intelligent’ support to help students to improve their models and gain modeling expertise. The article concludes with a discussion of our current efforts to improve the advice system based on the lessons learnt, which suggest extension of the range of solution representations and of the operations of the advice method.     

Applying learning behavioral Petri nets to the analysis of learning behavior in web-based learning environments

This paper proposes the learning behavioral Petri nets (LBPN) to model learning behavior in web-based environments. Fully useful records of learning behaviors must contain their expended time and corresponding contents. Therefore, the LBPN extends the colored tokens of colored Petri nets to identify learners and learning contents, and raises the time variable to represent diverse learning times for individual learners. To verify the viability of the LBPN, this paper also proposes a LBPN-based learning behavioral model to simulate a situation in which many learners participate in an e-learning course, and then to generate their behavioral patterns. The experimental results illustrated in this paper confirm that (1) the generated behavioral pattern based on the LBPN-based model is very close to actual data, (2) the time and cost spent to verify the effectiveness of an ITS is substantially reduced, (3) adequate testing data for estimating the performance and accuracy of an ITS is easily acquired, and (4) the LBPN-based model can be built to recommend appropriate learning contents and to accomplish adaptive learning.     

Enhancement of student learning performance using personalized diagnosis and remedial learning system

Although conventional student assessments are extremely convenient for calculating student scores, they do not conceptualize how students organize their knowledge. Therefore, teachers and students rarely understand how to improve their future learning progress. The limitations of conventional testing methods indicate the importance of accurately assessing and representing student knowledge structures. The personalized diagnosis and remedial learning system (PDRLS) proposed in this study enhances the effectiveness of the Pathfinder network by providing remedial learning paths for individual learners based on their knowledge structure. The sample was 145 students enrolled in introductory JAVA programming language courses at a Central Taiwan technology university. The experimental results demonstrate that learners who received personalized remedial learning guidance via PDRLS achieved improved learning performance, self-efficacy, and PDRLS use intention. The experimental results also indicated that students with lower knowledge level gain more benefits from the PDRLS than those with higher level of knowledge and that field dependence (FD) students obtain a greater benefit from PDRLS than field independence (FI) students do.     

A multi-user desktop virtual environment for teaching shop-keeping to children

Virtual role-playing environments can be a powerful mechanism of instruction, provided they are constructed such that learning how to play and win the game contributes to a player’s understanding of real-world concepts and procedures. North Dakota State University (NDSU) provides students with environments to enhance their understanding of geology (Planet Oit), cellular biology (Virtual Cell), programming languages (ProgrammingLand), retailing (Dollar Bay), and history (Blackwood). These systems present a number of opportunities and an equal number of challenges. Players are afforded a role-based, multi-user, ‘learn-by-doing’ experience, with software agents acting as both environmental effects and tutors and the possibilities of multi-user cooperation and collaboration. However, technical issues and one important cultural issue present a range of difficulties. The Dollar Bay environment, its particular challenges, and the solutions to these are presented.     

Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011

Implementing instructional interventions to accommodate learner differences has received considerable attention. Among these individual difference variables, the empirical evidence regarding the pedagogical value of learning styles has been questioned, but the research on the issue continues. Recent developments in Web-based implementations have led scholars to reconsider the learning style research in adaptive systems. The current study involved a content analysis of recent studies on adaptive educational hypermedia (AEH) which addressed learning styles. After an extensive search on electronic databases, seventy studies were selected and exposed to a document analysis. Study features were classified under several themes such as the research purposes, methodology, features of adaptive interventions and student modeling, and findings. The analysis revealed that the majority of studies proposed a framework or model for adaptivity whereas few studies addressed the effectiveness of learning style-based AEH. Scales were used for learning style identification more than automatic student modeling. One third of the studies provided a framework without empirical evaluation with students. Findings on concrete learning outcomes were not strong enough; however, several studies revealed that suggested models influenced student satisfaction and success. Current trends, potential research gaps and implications were discussed.     

The worked-example effect: Not an artefact of lousy control conditions

Recently it has been argued that the worked-example effect, as postulated by Cognitive Load Theory, might only occur when compared to unsupported problem-solving, but not when compared to well-supported or tutored problem-solving as instantiated, for example, in Cognitive Tutors. In two experiments, we compared a standard Cognitive Tutor with a version that was enriched with faded worked examples. In Experiment 1, students in the example condition needed less learning time to acquire a comparable amount of procedural skills and conceptual understanding. In Experiment 2, the efficiency advantage was replicated. In addition, students in the example condition acquired a deeper conceptual understanding. The present findings demonstrate that the worked-example effect is indeed robust and can be found even when compared to well-supported learning by problem-solving.     

A genetic fuzzy expert system for automatic question classification in a competitive learning environment

Intelligent tutoring systems are efficient tools to automatically adapt the learning process to the student’s progress and needs. One of the possible adaptations is to apply an adaptive question sequencing system, which matches the difficulty of the questions to the student’s knowledge level. In this context, it is important to correctly classify the questions to be presented to students according to their difficulty level. Many systems have been developed for estimating the difficulty of questions. However the variety in the application environments makes difficult to apply the existing solutions directly to other applications. Therefore, a specific solution has been designed in order to determine the difficulty level of open questions in an automatic and objective way. This solution can be applied to activities with special temporal and running features, as the contests developed through QUESTOURnament, which is a tool integrated into the e-learning platform Moodle. The proposed solution is a fuzzy expert system that uses a genetic algorithm in order to characterize each difficulty level. From the output of the algorithm, it defines the fuzzy rules that are used to classify the questions. Data registered from a competitive activity in a Telecommunications Engineering course have been used in order to validate the system against a group of experts. Results show that the system performs successfully. Therefore, it can be concluded that the system is able to do the questions classification labour in a competitive learning environment.     

智能教学系统NKI-Tutor的知识查询设计

NKI-Tutor是国家知识基础设施面向应用的智能教学系统，其功能是将NKI丰富的知识传授给用户。知识查询是NKI-Tulor给用户提供的一种查询式学习模式。该文研究如何通过NKI-Tutor的知识查询界面实现用户与智能教学系统的交互而获得所需知识，提出了基于自然语言理解的知识查询方法，用于构造和实现NKI-Tutor中用户查询知识的交互界面。该方法包括3方面内容：(1)分析用户的知识查询形式，采集用户查询句型模板；(2)根据用户查询的知识特征，把知识查询映射到概念．关系模型上；(3)通过智能分词和模糊匹配构造查询结果。     

Ontology based approach to Bayesian student model design

Probabilistic student model based on Bayesian network enables making conclusions about the state of student’s knowledge and further learning and teaching process depends on these conclusions. To implement the Bayesian network into a student model, it is necessary to determine “a priori” probability of the root nodes, as well as, the conditional probabilities of all other nodes. In our approach, we enable non-empirical mathematical determination of conditional probabilities, while “a priory” probabilities are empirically determined based on the knowledge test results. The concepts that are believed to have been learned or not learned represent the evidence. Based on the evidence, it is concluded which concepts need to be re-learned, and which not. The study described in this paper has examined 15 ontologically based Bayesian student models. In each model, special attention has been devoted to defining “a priori” probabilities, conditional probabilities and the way the evidences are set in order to test the successfulness of student knowledge prediction. Finally, the obtained results are analyzed and the guidelines for ontology based Bayesian student model design are presented.