Bayesian networks for student model engineering

Bayesian networks are graphical modeling tools that have been proven very powerful in a variety of application contexts. The purpose of this paper is to provide education practitioners with the background and examples needed to understand Bayesian networks and use them to design and implement student models. The student model is the key component of any adaptive tutoring system, as it stores all the information about the student (for example, knowledge, interest, learning styles, etc.) so the tutoring system can use this information to provide personalized instruction. Basic and advanced concepts and techniques are introduced and applied in the context of typical student modeling problems. A repertoire of models of varying complexity is discussed. To illustrate the proposed methodology a Bayesian Student Model for the Simplex algorithm is developed.     

PeRSIVA: An empirical evaluation method of a student model of an intelligent e-learning environment for computer programming

In this paper an evaluation method for assessing the effectiveness, accuracy and validity of a student model was presented. Our method is called PeRSIVA and is a combination of the well-known evaluation method of Kirkpatrick and the layered evaluation framework. These well-known and commonly used evaluation techniques have been selected in order to design an accurate and correct evaluation methodology, since there are no clear guidelines in the literature for the evaluation of the student model of an adaptive tutoring system. Furthermore, PeRSIVA method was used to evaluate the hybrid student model, which combines an overlay model with stereotypes and fuzzy logic techniques, of an e-learning system. Particularly, PeRSIVA assesses the results of student modeling in students' satisfaction, performance, progress, behavior and state, as well as the validity of the conclusions drawn by the student model and the validity of the adaptation decision making. The e-learning system was used by the students of a postgraduate program in the field of informatics in the University of Piraeus and the evaluation results demonstrated learning improvements in students and adaptation success to students' needs.     

Modelling the student in Pitagora 2.0

With the aim to individualise human-computer interaction, an Intelligent Tutoring System (ITS) has to keep track of what and how the student has learned. Hence, it is necessary to maintain a Student Model (SM) dealing with complex knowledge representation, such as incomplete and inconsistent knowledge and belief revision. With this in view, the main objective of this paper is to present and discuss the student modelling approach we have adopted to implement Pitagora 2.0, an ITS based on a co-operative learning model, and designed to support teaching-learning activities in a Euclidean Geometry context. In particular, this approach has led us to develop two distinct modules that cooperate to implement the SM of Pitagora 2.0. The first module resembles a classical student model, in the sense that it maintains a representation of the current student knowledge level, which can be used by the teacher in order to tune its teaching strategies to the specific student needs. In addition, our system contains a second module that implements a virtual partner, called companion. This module consists of a computational model of an average student which cooperates with the student during the learning process. The above mentioned module calls for the use of machine learning algorithms that allow the companion to improve in parallel with the real student. Computational results obtained when testing this module in simulation experiments are also presented.     

Evaluating Learning and Interactions in a Multimedia Environment

An empirical study was undertaken to evaluate second language learning with a videodisc named Vi-Conte. The 78 subjects were post-secondary students and adults and belonged either to a control group, a video-control group or an experimental group. The research methodology is presented as well as analyses of learners' navigational patterns, strategies, gains in vocabulary items, changes in attitude and global evaluation of the videodisc. Suggestions for the development of adaptive learning environments are made based on these findings. This revised version was published online in July 2006 with corrections to the Cover Date.     

采用贝叶斯网络建立一种智能学习环境学生代理模型的研究

贝叶斯（Bayesian）网络近年成为智能代理引人注目的研究方向,本文在介绍Bayesian网络及其构建方法、学习方法的基础上,构建了一个通用的可扩展的智能学习环境中的学生模型。     

Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education

Learners need to have good reasons to engage and accept e-learning. They need to understand that unless they do, the outcomes will be less favourable. The technology acceptance model (TAM) is the most widely recognized model addressing why users accept or reject technology. This study describes the development and evaluation of a virtual environment, the online 3D world Second Life (SL), for learning rapid sequence intubation (RSI). RSI is an increasingly frequently used method of acute airway management in healthcare settings. The intention of learners to use the system was explored based on the TAM, with the computer self-efficacy construct as an external variable. Two hundred and six nursing students participated in this study. The findings suggest that the system was perceived as useful, and that the students felt confident working with computers and intended to review RSI in SL as often as needed. However, they remained neutral regarding the ease of use of the system. Strategies were suggested for boosting the students’ self-confidence in using the system. Overall use of the TAM in this context was successful, indicating the robustness of the model. The limitations of the study were discussed and further areas of research on the TAM were proposed.     

Predictive student model supported by fuzzy-causal knowledge and inference

In this article we explore the paradigm of student-centered education. The aim is to enhance the learning of students by the self-adaptation of a Web-based educational system (WBES). The adaptive system’s behavior is achieved as a result of the decisions made by a student model (SM). The decision reveals the lecture option most suitable to teach a concept according to the student’s profile. Thus, the lecture content is authored from different view points (e.g. learning theory, type of media, complexity level, and user-system interaction degree). The purpose is to tailor several educational options to teach a given concept. Thereby, the SM elicits psychological attributes of the student to describe subjective traits, such as: cognitive, personality, and learning preferences. It also depicts pedagogical properties of the available lecture’s options. Moreover, the SM dynamically builds a cognitive map (CM) to set fuzzy-causal relationships among the lecture’s option properties and the student’s attributes. Based on a fuzzy-causal engine, the SM predicts the bias that a lecture’s option exerts on the student’s apprenticeship. The conceptual, theoretical, and formal grounds of the approach were tested by a computer implementation of the SM and an experiment. As a result of a field trial, we found that: the average learning acquired by an experimental group of volunteers that used this approach was 17% higher than the average apprenticeship of another equivalent control group, whose lectures were randomly chosen. Thus we conclude that: learning is better stimulated when the delivered lectures account a student’s profile than when they ignore it.     

Intelligent computer-assisted language learning: A bibliography

This bibliography focuses on works discussing intelligent computer-assisted language learning (ICALL). It includes over 200 entries divided into three sections: (1) collections, special issues and bibliographies, (2) general/theoretical works, (3) specific applications.Alan Bailin (Ph.D., McGill) is Associate Editor ofComputers and the Humanities. He has participated in the field of Intelligent Computer-Assisted Language Learning as a writer, editor, and researcher. He has also published in the areas of linguistics and semiotics.     

Intelligent preference selection model based on NRE for evaluating student learning achievement

In order to evaluate student learning achievement, several aspects should be considered, such as exercises, examinations, and observations. Traditionally, such an evaluation calculates a final score using a weighted average method after awarding numerical scores, and then determines a grade according to a set of established crisp criteria. However, this approach lacks the potential to reflect the individual characteristics of a class compared to others. Several researches have used fuzzy techniques to devise practical methods for evaluating student learning achievement to ascertain linguistic terms that are usually used by teachers to assess student learning achievement. However, these approaches are largely based on expert opinions and require complicated computational processes. In this paper, we present a new method for evaluating student learning achievement using an adaptive ordered weighted averaging operator and K-nearest-neighbor classification method. The proposed method simulates the evaluation behavior of teachers when performing a student achievement evaluation based on a norm-referenced evaluation by identifying situations involving the application of intelligence and provides a useful means to award a reasonable grade to students. Furthermore, the proposed method provides a feedback mechanism to update the norm dataset. Therefore, the repetitious use of the feedback mechanism will gradually strengthen the representativeness of the norm dataset.     

Information tradeoffs in model building: A network routing application

An integral part of the model-building process is the modeler's choice of how much information to gather and encode in the decision model. Obtaining more detailed and accurate information enables a more precise problem representation which, in turn, leads to more effective decision making. However, acquiring extensive and accurate information entails higher costs and delays. This paper uses a network routing decision context to illustrate the tradeoff between model precision and decision effectiveness, and explores a formal decision-theoretic approach to determine an appropriate model specification that balances information gathering costs and decision quality. We propose optimal and heuristic methods for generating good information search strategies, and report computational results based on random test problems. Our results highlight the importance of simultaneously considering information costs and decision payoffs for constructing a decision model to support routing decisions. The issues raised in this paper are especially significant for modeling dynamic, real-time decision contexts where delays induced by information gathering activities could have significant economic impact.     

The effect of blended learning on the development of clause combining as an aspect of the acquisition of written discourse by Jordanian learners of English as a foreign language

This study investigates the effect of blended learning on the development of clause combining as an aspect of the acquisition of written discourse by Jordanians who are learning English as a foreign language. Sixty participants majoring in English language at the University of Jordan took part in this study. The participants were divided into treatment and control groups, and the experimental design of a pretest and posttest was employed to test the effectiveness of blended learning on the ability of these participants to combine clauses in English using parataxis or subordination through a writing task. The results reveal that the treatment group outperformed their control group counterpart on the posttest in terms of verb morphology, distinguishing between nucleus and subordinate clauses, use of parataxis, use of subordinate clauses for temporal reference and to structure information, among others. The results also show that the use of technological devices improved students' attitudes to learning clause combining and made the learning process easier and more enjoyable. We suggest that blended learning develops the techniques of clause combining by learners of English as a foreign language, which in turn enhances their discourse knowledge in the target language and positively affects their ability to package information.     

IHSMTS中实例模式获取机制的设计与实现

IHSMTS是一种交互式多策略机器翻译系统。给出了该系统中模式知识获取机制的设计和实现方法，在这种模式知识获取机制中有机地结合了多种知识获取方式，包括：人机交互方式、基于机器翻译的源译文对照信息的自动获取、模式精炼方式和基于先验双语语料对齐的批量方式等，并通过模式验证算法实现对冗余、冲突等的检查，从而保证加入到模式库中的模式知识的单调递增生，同时实现模式适应性和准确性的平衡，使系统能够对实例模式库进行动态添加完善，从而动态递增地改善翻译效果。     

The design and implementation of a holistic training model for language teacher education in a cyber face-to-face learning environment

This study reports a qualitative investigation that examines the design and implementation of a holistic teacher training model in a cyber face-to-face language learning context. To this end, this study first proposes an e-training model called the Practice, Reflection and Collaboration (PRC) model, and discusses the rationale and theoretical framework underpinning each key component of the model. This is followed by the discussion of how the model was implemented in a 12-week e-teacher training program with the participation of trainee teachers and real distance students. This analysis focuses on how each component of the proposed model worked in our case, and what mechanisms were employed to nurture the trainees’ professional and personal development to become competent and confident e-teachers.     

Influence of polling technologies on student engagement: An analysis of student motivation,academic performance,and brainwave data

This study compared clicker technology against mobile polling and the Just-in-Time Teaching (JiTT) strategy to investigate how these methods may differently affect students' anxiety, self-efficacy, engagement, academic performance, and attention and relaxation as indicated by brainwave activity. The study utilized a quasi-experimental research design. To assess the differences between the effects of clickers and mobile polling, the study collected data from two courses at a large research university in Taiwan in which 69 students used either clickers or mobile polling. The results showed that mobile polling along with the JiTT strategy and in-class polls reduce graduate students' anxiety, improve student outcomes in an environment comprising both graduate and undergraduate students, and increase students' attention during polling. However, brainwave data revealed that during the polling activities, students' attention in the clicker and mobile polling groups respectively increased and decreased. Students nowadays do not find smartphones a novelty; however, incorporating them into class is still a potentially effective way to increase student attention and provide a direct way for instructors to observe the learning effects of lectures and improve their teaching approach on that basis.     

E-Learning中情绪认知个性化学生模型的研究*

为了提高E-Learning情绪教学的适应性和教学效果,针对传统学生模型的不足,引入人格、学习情绪及学习风格。通过OCC三维情绪空间描述学习情绪和丹尼尔·沙博人格划分理论进行情绪调节,通过美国心理学家布鲁姆的认知理论描述学生的认知能力,通过Felder-Silverman学习风格并结合支持向量机技术描述学习偏好的个性化特征。将情绪、认知、学习风格相结合构建一个完善的适合E-Learning教学的学生模型。通过将此学生模型应用到E-Learning教学中,不仅可以解决网络教学系统的情感缺失,而且大大提高了实     

Motivation in online learning: Testing a model of self-determination theory

As high attrition rates becomes a pressing issue of online learning and a major concern of online educators, it is important to investigate online learner motivation, including its antecedents and outcomes. Drawing on Deci and Ryan’s self-determination theory, this study proposed and tested a model for online learner motivation in two online certificate programs (N = 262). Results from structural equation modeling provided evidence for the mediating effect of need satisfaction between contextual support and motivation/self-determination; however, motivation/self-determination failed to predict learning outcomes. Additionally, this study supported SDT’s main theorizing that intrinsic motivation, extrinsic motivation, and amotivation are distinctive constructs, and found that the direct effect and indirect effects of contextual support exerted opposite impacts on learning outcomes. Implications for online learner support were discussed.     

The correlates of the digital divide and their impact on college student learning

By focusing on two dimensions of the digital divide—computer use and computer knowledge, this study explores four research questions: (1) What are the undergraduates doing with the computers they use at colleges? (2) How do undergraduates perform in regard to computer knowledge and skills? (3) With what is the digital divide among college students correlated? (4) What consequences does the digital divide have for student academic performance? In order to answer these research questions, a national survey was conducted. The survey investigated 3083 first-year college students of 12 4-year universities in Taiwan. A total of 2719 of them completed the questionnaires resulting in a response rate of 88.2%. In this study, the digital divide is measured in terms of computer use, which includes a variety of purposes for using computers and academic-related work as a proportion of total computer hours, and computer knowledge. Multiple regressions and a generalized ordered logit, i.e. a partial proportional odds model, are employed. The main findings include the following: (1) Undergraduates use computers not only for fulfilling their academic requirements and searching for information, but also for entertainment. On average, undergraduates spend about 19 h per week using computers, of which 5 h are academic-related. (2) Most undergraduates perform at the middle average level in terms of computer knowledge. (3) No significant differences among correlates in relating to demographic and socioeconomic family background were found in predicting the various purposes in using computers. (4) Students who are female, whose fathers and/or whose mothers are from minorities, whose fathers are blue-collar workers or unemployed, who study in the fields of the humanities and social sciences, and who enter private universities are at a disadvantage in terms of computer skills and knowledge. However, female students, students whose mothers were less educated and students who enroll in private universities are more focused computer users in terms of allocating time to academic-related work. (5) Computer knowledge and devotion to using computers for academic-related work have a moderate effect on college student learning, while the various other uses of computers do not. Of the different kinds of computer knowledge, it is the knowledge of software that helps students to learn the most.     

Investigating the quality of student approaches to using technology in experiences of learning through writing

This study reports on the student experience of learning through writing in an undergraduate science subject. During their writing experience, 52 first year university science students used a writing database, bulletin board and word-processor. Using quantitative questionnaires developed from student learning research, this study investigates the quality of the approaches adopted by students to the use of the technologies and how this related to the quality of their whole experience and performance measures. The results show that students who adopted a surface or reproductive approach tended to achieve relatively poorer learning outcomes and lower performance measures than students who adopted approaches which reflected understanding. The findings have important implications for teachers introducing technologies into writing processes for the purpose of improving students’ learning outcomes.