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.     

Intelligent assistance for teachers in collaborative e-learning environments

Collaborative learning environments provide a set of tools for students acting in groups to interact and accomplish an assigned task. In this kind of systems, students are free to express and communicate with each other, which usually lead to collaboration and communication problems that may require the intervention of a teacher. In this article, we introduce an intelligent agent approach to assist teachers through monitoring participations made by students within a collaborative distance learning environment, detecting conflictive situations in which a teacher’s intervention may be necessary. High precision rates achieved on conflict detection scenarios suggest great potential for the application of the proposed rule-based approach for providing personalized assistance to teachers during the development of group works.     

Providing adaptation and guidance for design learning by problem solving: The design planning approach in DomoSim-TPC environment

Experimental learning environments based on simulation usually require monitoring and adaptation to the actions the users carry out. Some systems provide this functionality, but they do so in a way which is static or cannot be applied to problem solving tasks. In response to this problem, we propose a method based on the use of intermediate languages to provide adaptation in design learning scenarios. Although we use some approaches which are familiar from other domains (e.g., programming tutors) they are novel as regards their application to a very different domain and as a result we have incorporated new strategies. The purpose of our proposal is to provide monitoring, guidance and adaptive features for PlanEdit, a tool for the learning of integral automation methods in buildings and housing by design. This tool is part of a collaborative environment, called DomoSim-TPC, which supports distance learning of domotical design. We have carried out an experiment to obtain some data which confirm that our position can be effective for group learning of domotical design, studying the relationship between the quantity of model work carried out and the errors made.     

Data mining for providing a personalized learning path in creativity: An application of decision trees

Customizing a learning environment to optimize personal learning has recently become a popular trend in e-learning. Because creativity has become an essential skill in the current e-learning epoch, this study aims to develop a personalized creativity learning system (PCLS) that is based on the data mining technique of decision trees to provide personalized learning paths for optimizing the performance of creativity. The PCLS includes a series of creativity tasks as well as a questionnaire regarding several key variables. Ninety-two college students were included in this study to examine the effectiveness of the PCLS. The experimental results show that, when the learning path suggested by a hybrid decision tree is employed, the learners have a 90% probability of obtaining an above-average creativity score, which suggests that the employed data mining technique can be a good vehicle for providing adaptive learning that is related to creativity. Moreover, the findings in this study shed light on what components should be accounted for when designing a personalized creativity learning system as well as how to integrate personalized learning and game-based learning into a creative learning program to maximize learner motivation and learning effects.     

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.     

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.     

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.     

The influence of student characteristics on the use of adaptive e-learning material

Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive e-learning materials. Ninety-four students participated in the study. We determined characteristics in a heterogeneous student group by collecting demographic data and measuring motivation and prior knowledge. We also measured the learning paths students followed and learning strategies they used when working with adaptive e-learning material in a molecular biology course. We then combined these data to study if and how student characteristics relate to the learning paths and strategies they used. We observed that students did follow different learning paths. Gender did not have an effect, but (mainly Dutch) BSc students differed from (international) MSc students in the intrinsic motivation they had and the learning paths and strategies they followed when using the adaptive e-learning material.     

Enhancing e-learning effectiveness using an intelligent agent-supported personalized virtual learning environment: An empirical investigation

Virtual learning environments (VLEs) developed under constructivism and embedded personalization learning functions have the potential to meet different requirements of different learners and thus increase e-Learning effectiveness. We formulated internal personalized learning mechanisms by implementing intelligent agents in a VLE under a constructivist learning model and further developed an e-learning effectiveness framework by integrating educational and IS theories. An empirical field experiment involving 228 university students was conducted. The findings suggested that personalized e-learning facilities enhance online learning effectiveness in terms of examination, satisfaction, and self-efficacy criteria.     

Controlled experiment replication in evaluation of e-learning system’s educational influence

We believe that every effectiveness evaluation should be replicated at least in order to verify the original results and to indicate evaluated e-learning system’s advantages or disadvantages. This paper presents the methodology for conducting controlled experiment replication, as well as, results of a controlled experiment and an internal replication that investigated the effectiveness of intelligent authoring shell eXtended Tutor–Expert System (xTEx-Sys). The initial and the replicated experiment were based on our approach that combines classical two-group experimental design and with factoral design. A trait that distinguishes this approach from others is the existence of arbitrary number of checkpoint-tests to determine the effectiveness in intermediate states. We call it a pre-and-post test control group experimental design with checkpoint-tests. The gained results revealed small or even negative effect sizes, which could be explained by the fact that the xTEx-Sys’s domain knowledge presentation is rather novel for students and therefore difficult to grasp and apply in earlier phases of the experiment. In order to develop and improve the xTEx-Sys, further experiments must be conducted.     

Computational organization of didactic contents for personalized virtual learning environments

This paper presents an organization model for personalized didactic contents used in individual study environments. For many students the availability of contents in a general form might not be effective. A multilevel structure of concepts is proposed to provide different presentation combinations of the same content. Our work shows that it is possible to personalize the didactic content in order to encourage students, by using proximal learning patterns. These patterns are obtained from the analysis of the actions of students with positive results in the individual content organization. The system uses artificial intelligence techniques to reactively organize and personalize content. Personalization is made possible by means of an artificial neural network that classifies the student's profile and assigns it a proximal learning pattern. Expert rules are used to mediate and adjust the contents reactively. Experimental results indicate that the approach is efficient and provides the student a better use of the content with adaptive and reactive personalized presentation.     

Item difficulty estimation: An auspicious collaboration between data and judgment

The evolution from static to dynamic electronic learning environments has stimulated the research on adaptive item sequencing. A prerequisite for adaptive item sequencing, in which the difficulty of the item is constantly matched to the ability level of the learner, is to have items with a known difficulty level. The difficulty level can be estimated by means of the item response theory (IRT). However, the requirement of a large sample size for calibrating items based on IRT models is not easily met in many practical learning situations. The aim of this paper is to search for relatively simple and fast alternative estimation methods and to review the accuracy of these methods as compared to IRT-based calibration in one single setting, and this for various sample sizes. Using real data, six alternative estimation methods are compared next to IRT-based calibration: proportion correct, learner feedback, expert rating, one-to-many comparison (learner), one-to-many comparison (expert) and the Elo rating system. Results indicate that proportion correct has the strongest relation with IRT-based difficulty estimates, followed by learner feedback, the Elo rating system, expert rating and finally one-to-many comparison. Learner feedback and one-to-many comparison (learner) provide stable estimates even with a small sample size. IRT, proportion correct and the Elo rating system provide reliable estimates, especially with a sample size of 200-250 learners. The alternative estimation methods can be utilized for adaptive item sequencing when IRT-based calibration does not yet provide reliable estimates or can be used as a prior in a Bayesian estimation method.     

Intelligent web-based learning system with personalized learning path guidance

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths in order to promote the learning performance of individual learners. However, most personalized e-learning systems usually neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other while performing personalized learning services. Moreover, the problem of concept continuity of learning paths also needs to be considered while implementing personalized curriculum sequencing because smooth learning paths enhance the linked strength between learning concepts. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Therefore, compared to the freely browsing learning mode without any personalized learning path guidance used in most web-based learning systems, this paper assesses whether the proposed genetic-based personalized e-learning system, which can generate appropriate learning paths according to the incorrect testing responses of an individual learner in a pre-test, provides benefits in terms of learning performance promotion while learning. Based on the results of pre-test, the proposed genetic-based personalized e-learning system can conduct personalized curriculum sequencing through simultaneously considering courseware difficulty level and the concept continuity of learning paths to support web-based learning. Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.     

Performance evaluation of an online argumentation learning assistance agent

Recent research indicated that students’ ability to construct evidence-based explanations in classrooms through scientific inquiry is critical to successful science education. Structured argumentation support environments have been built and used in scientific discourse in the literature. To the best of our knowledge, no research work in the literature addressed the issue of automatically assessing the student’s argumentation quality, and the teaching load of the teacher that used the online argumentation support environments is not alleviated. In this work, an intelligent argumentation assessment system based on machine learning techniques for computer supported cooperative learning is proposed. Learners’ arguments on discussion board were examined by using argumentation element sequence to detect whether the learners address the expected discussion issues and to determine the argumentation skill level achieved by the learner. Learners are first assigned to heterogeneous groups based on their responses to the learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. A feedback rule construction mechanism is used to issue feedback messages to the learners in case the argumentation assessment system detects that the learners go in a biased direction. The Moodle, an open source software e-learning platform, was used to establish the cooperative learning environment for this study. The experimental results exhibit that the proposed work is effective in classifying and improving student’s argumentation level and assisting the students in learning the core concepts taught at a natural science course on the elementary school level.     

Designing a smartphone app to teach English (L2) vocabulary

The researcher developed a Basic4Android smartphone app (named as Word Learning-CET6) and investigated its effectiveness as a tool in helping English as a Foreign Language college students learn English vocabulary. The app, containing 1274 English words, was designed to be installed into smartphones with Android operating system. To test the program's effectiveness, two groups of students were set up as a test group (those with Word Learning-CET6) and a control group (those without Word Learning-CET6). Knowledge of the vocabulary was tested before and after the study to assess the impact of the program. The study showed that the students using the program significantly outperformed those in the control group in acquiring new vocabulary. At the conclusion of this study, the researcher designed an app and established a pedagogical paradigm which can be followed as a way of mobile learning.     

Future integrated learning environments with multimedia

Abstract  Recent progress in information technology hardware and the spread of the Internet have opened a variety of new ways for many fields. Although slower than the business field to catch up with these new developments, the educational field has gradually migrated towards the World-wide web, mostly under the slogan of free, accessible education, to and from anyplace, at anytime. This development triggered an important shift from the teaching paradigm to the learning paradigm. However, slow network speed hindered the first learning environments from being more than simple, electronic text-books. The latest trends making use of increased bandwidths and integrating various media to enhance learning. Moreover, for obtaining learner-oriented, customised learning environ-ments, intelligent tutoring techniques are being adapted and developed for the web. This paper presents these trends on one hand, but on the other hand, also addresses the dangers and pitfalls that such an avalanche of change can bring and stresses the task of ensuring that the real goal of enhancing and improving learning is not overlooked.     

A perspective on learning for intelligent educational systems*

Abstract In this paper two observations are the starting points for a proposal as to how learning, rather than computing considerations, can be made the main focus in the development of Intelligent Educational Systems (IESs). These observations are: a) the predominant use of computers in education is for the running of general tool software; and b) the symbolic approach to Artificial Intelligence is being questioned from a situated cognition perspective.
Learning interactions are seen as consisting of a Task Level, and a higher-level Discussion Level comprising planning and evaluation of, and reflection about, Task Level activity. It is proposed that IESs should be developed by adding a module to support Discussion Level interaction with the learner. The Mayday project is described, in which expert human teachers are being studied as they support Discussion Level with a learner working at computer-based lexical activities. It is argued that such studies of human learning is a necessary step towards the development of the proposed IESs.     

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.     

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent “imitate” teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments.