On Predicting Learning Styles in Conversational Intelligent Tutoring Systems using Fuzzy Classification Trees

Oscar is a conversational intelligent tutoring system (CITS) which dynamically predicts and adapts to a student's learning style throughout the tutoring conversation. Oscar aims to mimic a human tutor to improve the effectiveness of the learning experience by leading a natural language tutorial and adapting material to suit an individual's learning style. Prediction of learning style is undertaken through capturing independent variables during the conversation. The variable with the highest value determines the individuals learning style. This paper proposes a new method which uses a fuzzy classification tree to build a fuzzy predictive model using these variables which are captured through natural language dialogue Experiments have been undertaken on two of the learning style dimensions: perception (sensory-intuitive) and understanding (sequential-global). Early results show the model has substantially increased the predictive accuracy of the Oscar CITS and discovered some interesting relationships amongst these variables

On Predicting Learning Styles in Conversational Intelligent Tutoring Systems using Fuzzy Decision Trees

Intelligent Tutoring Systems personalise learning for students with different backgrounds, abilities, behaviours and knowledge. One way to personalise learning is through consideration of individual differences in preferred learning style. OSCAR is the name of a Conversational Intelligent Tutoring System that models a person's learning style using natural language dialogue during tutoring in order to dynamically predict, and personalise, their tutoring session. Prediction of learning style is undertaken by capturing independent behaviour variables during the tutoring conversation with the highest value variable determining the student's learning style. A weakness of this approach is that it does not take into consideration the interactions between behaviour variables and, due to the uncertainty inherently present in modelling learning styles, small differences in behaviour can lead to incorrect predictions. Consequently, the learner is presented with tutoring material not suited to their learning style. This paper proposes a new method that uses fuzzy decision trees to build a series of fuzzy predictive models combining these variables for all dimensions of the Felder Silverman Learning Styles model. Results using live data show the fuzzy models have increased the predictive accuracy of OSCAR-CITS across four learning style dimensions and facilitated the discovery of some interesting relationships amongst behaviour variables

Detection of learning styles with prior knowledge data using the SVM, K-NN and Naïve Bayes algorithms

The two types of automatic learning style detection approaches are data driven (DD) and literature based (LB). Both methods of automatic learning style detection have advantages over traditional learning style detection methods because they use external data sources, such as forums, quizzes and views of teaching materials, that are more accurate than the questionnaires used in traditional styles of detection. The results of automatic detection, on the other hand, do not always reflect learning styles. This paper presents a learning style recognition method that uses data from the learner’s internal source, namely prior knowledge, to overcome these challenges. Prior knowledge is proposed because it is based on the learner’s knowledge or skills, which better reflect the learner’s characteristics, rather than on the learner’s behaviour, which tends to be dynamic. By using past knowledge, this paper presents a method for detecting automatic learning patterns. The learning style detection framework is unique in that it consists of three stages: prior knowledge question development, prior knowledge measurement and learning style detection using the Support Vector Machine (SVM), Naïve Bayes and K-Nearest Neighbour (K-NN) classification methods. The accuracy of learning style detection using prior knowledge data was higher than detection results using behavioural data or hybrid data (prior knowledge + behaviour) in this studyMetode deteksi gaya belajar otomatis dapat dibagi menjadi dua yakni Metode Data-Driven (DD) dan Literature Based (LB). Kedua metode deteksi gaya belajar otomatis tersebut memiliki kelebihan dibandingkan metode de- teksi gaya belajar konvensional, karena metode deteksi gaya belajar otomatis menggunakan sumber data eksternal seperti forum, kuis dan kunjungan bahan ajar yang lebih akurat dibandingkan dengan kuesioner yang digunakan dalam metode deteksi gaya belajar konvensional. Meskipun demikian, hasil deteksi otomatis tidak selalu mencerminkan gaya belajar.. Untuk mengatasi kendala tersebut, penelitian ini mengusulkan metode deteksi gaya belajar yang mengambil data dari sumber internal pembelajar yakni prior knowledge. Prior knowledge diusulkan karena tidak berdasarkan pada perilaku pembelajar yang cenderung bersifat dinamis, namun lebih kepada pengetahuan atau keterampilan yang dimiliki, yang lebih mencerminkan karakteristik pembelajar. Penelitian ini mengusulkan sebuah metode untuk mendeteksi gaya belajar otomatis dengan memanfaatkan prior knowledge. Kebaruannya terletak pada kerangka kerja (framework) deteksi gaya belajar yang terdiri dari tiga tahapan yaitu: tahapan pembangunan pertanyaan prior knowledge, tahapan pengukuran prior knowledge, dan tahapan deteksi gaya belajar dengan menggunakan metode klasifikasi svm, naïve bayes dan k-nn, Penelitian ini menghasilkan akurasi hasil deteksi gaya belajar dengan data prior knowledge yang lebih tinggi dibandingkan dengan hasil deteksi yang menggunakan data perilaku dan data hybrid (prior knowledge + perilaku

Say Hello to Your New Automated Tutor – A Structured Literature Review on Pedagogical Conversational Agents

In this paper, we present the current state of the art of using conversational agents for educational purposes. These so-called pedagogical conversational agents are a specialized type of e-learning and intelligent tutoring systems. The main difference to traditional e-learning and intelligent tutoring systems is that they interact with learners using natural language dialogs, e.g. in the form of chatbots. For the sake of our research project, we analyzed current trends in the research stream as well as research gaps. Our results show for instance that (1) there is a trend towards using mobile conversational agents in education, (2) a proper generalization of existing research results (e.g. design knowledge) is missing, and (3) there is a need for comprehensive in-depth evaluation studies and corresponding process models. Based on our results, we outline a research agenda for future research studies

Student Behavior Analysis to Predict Learning Styles Based Felder Silverman Model Using Ensemble Tree Method

Learning styles are very important to know so that students can learn effectively. By understanding the learning style, students will learn about their needs in the learning process. One of the famous learning management systems is called Moodle. Moodle can catch student experiences and behaviors while learning and store all student activities in the Moodle Log. There is a fundamental issue in e-learning where not all students have the same degree of comprehension. Therefore, in some cases of learning in E-Learning, students tend to leave the classroom and lack activeness in the classroom. In order to solve these problems, we have to know students' preferences in the learning process by understanding each student's learning style. To find out the appropriate student learning style, it is necessary to analyze student behavior based on the frequency of visits when accessing Moodle E-learning and fill out the Index Learning Style (ILS) questionnaire. The Felder Silverman model's learning style classifies it into four dimensions: Input, Processing, Perception, and Understanding. We propose a learning style prediction model using the Ensemble Tree method, namely Bagging and Boosting-Gradient Boosted Tree. Afterwards, we evaluate the classification results using Stratified Cross Validation and measure the performance using accuracy. The results showed that the Ensemble Tree method's classification efficiency has higher accuracy than a single tree classification model

Parameters and Structure of Neural Network Databases for Assessment of Learning Outcomes

The purpose of this study is to determine the methodology, develop a theory of construction, put into practice algorithmization and implement the functionality of a hybrid intelligent system for assessment of educational outcomes of trainees on the basis of the identified keyword parameters and structure of the artificial neural network using expert systems and fuzzy simulation; to develop a methodology for the construction of structural-logic, hierarchical, functional and fractal schemes for structuring databases of the didactic field of learning elements; to determine the content, structure of parameters and database components, selection criteria and the content of complexes of educational standards. The methodology of introducing intelligent systems into mathematical education is on the basis of the Hegelian triad: thesis (implementation of the coherence principle) – antithesis (implementation of principles of the fractality and historiogenesis) – synthesis (implementation of the principles of self-organization and reflection of the complex system inversion integrity). Requirements for the organization and construction of the artificial neural network for assessment of personal achievements on the basis of fuzzy simulation have been developed. In the direction of using elements of fractal geometry, the technological structures of clusters that constitute the basis of generalized structures have been developed. In particular, it is revealed that the didactic field of learning elements is equipped with a system of multi-level hierarchical databases of exercises, motivational-applied, research, practice-oriented tasks using expert systems and integration of mathematical, information, natural-science and humanities knowledge and procedures

An Architecture of Decision Support System for Visual-Auditory-Kinesthetic (VAK) Learning Styles Detection Through Behavioral Modelling

Learning style (LS) is a description of the attitudes and behaviors which determine an individual’s preferred way of learning. Since each student has different LS, it is important for the teacher to recognize the differences in LS. Thus, an appropriate technique to detect students' LS, improve the motivation and academic achievement are required. The common approach using questionnaires to identify LS is less accurate due to complete the questionnaire is a tedious task for students and tend to choose answers randomly without understanding the questions. Emotions such as anger, sadness, and happiness resulting the different questionnaire answers. Due to the approach constrains, this study has focused on automated approaches that identify student LS from student behavior in the learning process. Implementation of decision support system (DSS) as automated application systems is needed to help teachers make decisions in determining students' LS. Thus, the objective of this study is to propose the architecture of LS detection automatically using decision support system. The development of the architecture is applying the behavioral modelling, that are contained student’s behavior parameters for visual-auditory-kinesthetic (VAK) model. Evaluation of the architecture is tested with the precision DSS engine. The accuracy of the rule technique achieves significant 80% accuracy. This study aims to help teachers to identify the ability of the student through the learning style (LS) in order to create effectiveness of learning and improving student’s achievement indirectly. Keywords— decision support system, reasoning engines, learning style detection, user behavior, visual-auditory-kinesthetic (VAK) mode

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

Granular computing based approach of rule learning for binary classification

Rule learning is one of the most popular types of machine-learning approaches, which typically follow two main strategies: ‘divide and conquer’ and ‘separate and conquer’. The former strategy is aimed at induction of rules in the form of a decision tree, whereas the latter one is aimed at direct induction of if–then rules. Due to the case that the divide and conquer strategy could result in the replicated sub-tree problem, which not only leads to overfitting but also increases the computational complexity in classifying unseen instances, researchers have thus been motivated to develop rule learning approaches through the separate and conquer strategy. In this paper, we focus on investigation of the Prism algorithm, since it is a representative one that follows the separate and conquer strategy, and is aimed at learning a set of rules for each class in the setting of granular computing, where each class (referred to as target class) is viewed as a granule. The Prism algorithm shows highly comparable performance to the most popular algorithms, such as ID3 and C4.5, which follow the divide and conquer strategy. However, due to the need to learn a rule set for each class, Prism usually produces very complex rule-based classifiers. In real applications, there are many problems that involve one target class only, so it is not necessary to learn a rule set for each class, i.e., only a set of rules for the target class needs to be learned and a default rule is used to indicate the case of non-target classes. To address the above issues of Prism, we propose a new version of the algorithm referred to as PrismSTC, where ‘STC’ stands for ‘single target class’. Our experimental results show that PrismSTC leads to production of simpler rule-based classifiers without loss of accuracy in comparison with Prism. PrismSTC also demonstrates sufficiently good performance comparing with C4.5