Teaching Strategies and Ontologies for E-Learning

* The work is partially suported by Russian Foundation for Basic Studies (grant 02-01-00466).

Knowledge Learning Technology for Intelligent Tutoring Systems

In this work we suggest the technology of creation of intelligent tutoring systems which are oriented to teach knowledge. It is supposed the acquisition of expert’s knowledge by using of the Formal Concept Analysis method, then construction the test questions which are used for verification of the pupil's knowledge with the expert’s knowledge. Then the further tutoring strategy is generated by the results of this verification.

The Conception of Creation of Integrated Development Environment for Computer Training Systems

The various questions of creation of integrated development environment for computer training systems are considered in the given paper. The information technologies that can be used for creation of the integrated development environment are described. The different didactic aspects of realization of such systems are analyzed. The ways to improve the efficiency and quality of learning process with computer training systems for distance education are pointed.

A Computer-Based Strategy for Practical Skills Teaching

An adaptive learning technology embedded in e-learning environments ensures choice of the structure, content, and activities for each individual learner according to the teaching team’s domain and didactic knowledge and skills. In this paper a computer-based scenario for application of an adaptive navigation technology is proposed and demonstrated on an example course topic.

Technology-Enhanced Learning: Review and Prospects

This paper is a reflection on the history and future of technology-enhanced learning. Over the last century various new technologies were introduced in education. Often, educational revolutions were proclaimed. Unfortunately, most of these new technologies failed to meet the high expectations. This paper reviews the rise and fall of various "revolutionary" learning technologies and analyses what went wrong. Three main driving factors are identified that influence the educational system: 1) educational practice, 2) educational research, and 3) educational technology. The role and position of these factors is elaborated and critically reviewed. Today, again many promising new technologies are being put in place for learning: gaming, social web, and mobile technologies, for example. Inevitably, these are once again proclaimed by its supporters to revolutionise teaching and learning. The paper concludes with identifying a number of relevant factors that substantiate a favourable future outlook of technology-enhanced learning.

Education in Computer Science and the Role of the Teacher in the Environment of Open Educational Resources

Report published in the Proceedings of the National Conference on "Education in the Information Society", Plovdiv, May, 2013

Automatized Generation of Metadata and Calibrated Test Item Banks for E-learning

Report published in the Proceedings of the National Conference on "Education in the Information Society", Plovdiv, May, 2013

Manifold learning and the quantum Jensen-Shannon divergence kernel

The quantum Jensen-Shannon divergence kernel [1] was recently introduced in the context of unattributed graphs where it was shown to outperform several commonly used alternatives. In this paper, we study the separability properties of this kernel and we propose a way to compute a low-dimensional kernel embedding where the separation of the different classes is enhanced. The idea stems from the observation that the multidimensional scaling embeddings on this kernel show a strong horseshoe shape distribution, a pattern which is known to arise when long range distances are not estimated accurately. Here we propose to use Isomap to embed the graphs using only local distance information onto a new vectorial space with a higher class separability. The experimental evaluation shows the effectiveness of the proposed approach. © 2013 Springer-Verlag.

Supervised learning of graph structure

Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective Bayesian approach to attributed graph learning. We present a naïve node-observation model, where we make the important assumption that the observation of each node and each edge is independent of the others, then we propose an EM-like approach to learn a mixture of these models and a Minimum Message Length criterion for components selection. Moreover, in order to avoid the bias that could arise with a single estimation of the node correspondences, we decide to estimate the sampling probability over all the possible matches. Finally we show the utility of the proposed approach on popular computer vision tasks such as 2D and 3D shape recognition. © 2011 Springer-Verlag.

The effect of the use of computer assisted instruction (CAI) on attitudes and computational scores of developmental mathematics students at two inner city schools with predominantly Black enrollment

This study examined the effects of computer assisted instruction (CAI) 1 hour per week for 18 weeks on changes in computational scores and attitudes of developmental mathematics students at schools with predominantly Black enrollment. Comparisons were made between students using CAI with differing software--PLATO, CSR or both together--and students using traditional instruction (TI) only.^ This study was conducted in the Dade County Public School System from February through June 1991, at two senior high schools. The dependent variables, the State Student Assessment Test (SSAT), and the School Subjects Attitude Scales (SSAS), measured students' computational scores and attitudes toward mathematics in 3 categories: interest, usefulness, and difficulty, respectively.^ Univariate analyses of variance were performed on the least squares mean differences from pretest to posttest for testing main effects and interactions. A t-test measured significant main effects and interactions. Results were interpreted at the.01 level of significance.^ Null hypotheses 1, 2, and 3 compared versions of CAI with the control group, for changes in mathematical computation scores measured with the SSAT. It could not be concluded that changes in standardized mathematics test scores of students using CAI with differing software 1 hour per week for 18 class hours combined with TI were significantly higher than changes in test scores for students receiving TI only.^ Null hypotheses 4, 5, and 6 tested the effects of CAI for attitudes toward mathematics for experimental groups against control groups measured with the SSAS. Changes in attitudes toward mathematics of students using CAI with differing software 1 hour per week for 18 class hours combined with TI were not significantly higher than attitude changes for students receiving TI only.^ Teacher effect on students' computational scores was a more influential variable than CAI. No interaction was found between gender and learning method on standardized mathematics test scores (null hypothesis 7). ^

Effects of structured writing versus nonstructured writing as an effective and efficient teaching and learning strategy

This research investigated the effectiveness and efficiency of structured writing as compared to traditional nonstructured writing as a teaching and learning strategy in a training session for teachers.^ Structured writing is a method of identifying, interrelating, sequencing, and graphically displaying information on fields of a page or computer. It is an alternative for improving training and educational outcomes by providing an effective and efficient documentation methodology.^ The problem focuses upon the contradiction between: (a) the supportive research and theory to modify traditional methods of written documents and information presentation and (b) the existing paradigm to continue with traditional communication methods.^ A MANOVA was used to determine significant difference between a control and an experimental group in a posttest only experimental design. The experimental group received the treatment of structured writing materials during a training session. Two variables were analyzed. They were: (a) effectiveness; correct items on a posttest, and (b) efficiency; time spent on test.^ The quantitative data showed a difference for the experimental group on the two dependent variables. The experimental group completed the posttest in 2 minutes less time while scoring 1.5 more items correct. An interview with the training facilitators revealed that the structured writing materials were "user friendly." ^

The effect of an enriched learning community on success and retention in chemistry courses

Since the mid-1990s, the United States has experienced a shortage of scientists and engineers, declining numbers of students choosing these fields as majors, and low student success and retention rates in these disciplines. Learning theorists, educational researchers, and practitioners believe that learning environments can be created so that an improvement in the numbers of students who complete courses successfully could be attained (Astin, 1993; Magolda & Terenzini, n.d.; O'Banion, 1997). Learning communities do this by providing high expectations, academic and social support, feedback during the entire educational process, and involvement with faculty, other students, and the institution (Ketcheson & Levine, 1999). ^ A program evaluation of an existing learning community of science, mathematics, and engineering majors was conducted to determine the extent to which the program met its goals and was effective from faculty and student perspectives. The program provided laptop computers, peer tutors, supplemental instruction with and without computer software, small class size, opportunities for contact with specialists in selected career fields, a resource library, and Peer-Led Team Learning. During the two years the project has existed, success, retention, and next-course continuation rates were higher than in traditional courses. Faculty and student interviews indicated there were many affective accomplishments as well. ^ Success and retention rates for one learning community class ( n = 27) and one traditional class (n = 61) in chemistry were collected and compared using Pearson chi square procedures ( p = .05). No statistically significant difference was found between the two groups. Data from an open-ended student survey about how specific elements of their course experiences contributed to success and persistence were analyzed by coding the responses and comparing the learning community and traditional classes. Substantial differences were found in their perceptions about the lecture, the lab, other supports used for the course, contact with other students, helping them reach their potential, and their recommendation about the course to others. Because of the limitation of small sample size, these differences are reported in descriptive terms. ^

Effects of cooperative and individual integrated learning system on attitudes and achievement in mathematics

This study compares the effects of cooperative delivery (CD) and individual delivery (ID) of integrated learning system (ILS) instruction in mathematics on achievement, attitudes and behaviors in adult (16-21 yrs.) high school students (grades 9-13). The study was conducted in an urban adult high school in Miami-Dade County Public Schools using a pre-test/post-test design. Achievement was measured using the Test of Adult Basic Education (TABE) by CTB MC-Graw-Hill and Compass Learning. An attitudinal survey measured attitudes towards mathematics, the computer-related lessons, and attitudes toward group activities. Behavior was assessed using computer lab observations. ^ Two-way analyses of variance (ANOVA) were conducted on achievement (TABE and Compass) by group and time (pre and post). A one-way ANOVA was conducted on the overall attitude by group on the five components (i.e., content mathematics, delivery/computers, cooperative, partners, and self efficacy) and a one-way ANOVA was conducted on the on-task behavior by group. ^ The results of the study revealed that CD and ID students working on mathematics activities delivered by the ILS performed similarly on achievement tests of the TABE. The CD-ILS students had significantly better overall mathematics attitudes than the ID-ILS students and the ID-ILS group was on-task significantly more than the CD-ILS group. This study concludes that regularity and period of time over which the ILS is used may prove to be important variables although there were insufficient data to fully investigate the impact of models of use. Additionally, a minimum amount of time-on-system is necessary before gains can become apparent in innumeracy and increasing exposure to the system may have beneficial effects on learning. ^

Signal processing of physiological signals for automated assessment of stress in computer users

The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^

Group presence in virtual worlds: Supporting collaborative e-learning

Even though e-learning endeavors have significantly proliferated in recent years, current e-learning technologies provide poor support for group-oriented learning. The now popular virtual world's technologies offer a possible solution. Virtual worlds provide the users with a 3D - computer generated shared space in which they can meet and interact through their virtual representations. Virtual worlds are very successful in developing high levels of engagement, presence and group presence in the users. These elements are also desired in educational settings since they are expected to enhance performance. The goal of this research is to test the hypothesis that a virtual world learning environment provides better support for group-oriented collaborative e-learning than other learning environments, because it facilitates the emergence of group presence. To achieve this, a quasi-experimental study was conducted and data was gathered through the use of various survey instruments and a set of collaborative tasks assigned to the participants. Data was gathered on the dependent variables: Engagement, Group Presence, Individual Presence, Perceived Individual Presence, Perceived Group Presence and Performance. The data was analyzed using the statistical procedures of Factor Analysis, Path Analysis, Analysis of Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA). The study provides support for the hypothesis. The results also show that virtual world learning environments are better than other learning environments in supporting the development of all the dependent variables. It also shows that while only Individual Presence has a significant direct effect on Performance; it is highly correlated with both Engagement and Group Presence. This suggests that these are also important in regards to performance. Developers of e-learning endeavors and educators should incorporate virtual world technologies in their efforts in order to take advantage of the benefit they provide for e-learning group collaboration.