Student progress assessment with the help of an intelligent pupil analysis system

Students and lecturers would like to know how well students have learned the study materials being taught. A formal test or exam would cause needless stress for students. To resolve this problem, the authors of this article have developed an Intelligent Pupil Analysis (IPA) System. A sufficient amount of studies worldwide prove an interrelation between pupil size and a person's cognitive load. The obtained research results are comparable with the results from other similar studies. The original contribution of this article, compared to the research results published earlier, is as follows: the IPA System developed by the authors is superior to the traditional pupil analysis research due to the integration of pupil analysis with subsystems of decision support, recommender and intelligent tutoring systems and innovative Models of the Model-base, which permit a more detailed analysis of the knowledge attained by a student. This article ends with a case study to demonstrate the practical operation of the IPA System.     

Participation and common knowledge in a case study of student blogging

The interaction between participation and the emergence of common knowledge is the subject matter of this paper. A case study of a single class provides the focal point of analysis. During the semester the students participated in a blogging activity. As a result of their participation, the students create and distribute knowledge. The online efforts of the students can be described as participation in both a discourse and knowledge community. At one level, blogging is an activity composed of writing, reading, and commenting, and at a second level, the students share their thoughts in their own voices. At a third level, over the course of the semester, the student posts and commentary form a commons of information that can be mined later in the semester for other kinds of learning activities. Knowledge creation, distribution, and accumulation are analyzed in terms of student participation at both the level of individual events and from the perspective of an ongoing community.     

Affective Tutoring System for Built Environment Management

There are quite a lot of researche performed in the world, which prove that interest, stress and learning productivity of a learner to a large extent determine study results. Moreover, learning emotionality, pleasurableness and attractiveness, which help to increase its efficiency, are stressed by various researchers. Researchers have noticed, that a student's interest, stress, learning productivity and academic achievement are quite closely related. Experts have noticed that in order to cause interest, increase learning productivity and maintain stress in a rational level of a learner it is necessary to constantly change the learning subject with regard to situational and individual interest. For this reason, an Affective Tutoring System for Built ENvironment Management (ATEN), developed by the authors of the article is very suitable. The ATEN was developed and fine-tuned in the course of the TEMPUS project “Reformation of the Curricula on Built Environment in the Eastern Neighbouring Area”. One of the ATEN innovations is that the System integrates the self-assessment and self-esteem measurement of students with multimodal biometric and intelligent methodologies and technologies. Affective Tutoring System for Built Environment Management can create a rational version of a learning process tailored to a specific student, taking into account such factors as how much the studies are interesting or difficult and the level of stress (with the help of biometric technologies). The System includes an automatic function that takes module topics and compiles an optimal set of personalised materials for a specific student. The case study submitted in this article partly demonstrates this developed System.     

Recommender System to Analyze Student’s Academic Performance

A sufficient amount of studies worldwide prove an interrelation linking student learning productivity and interest in learning to physiological parameters. An interest in learning affects learning productivity, while physiological parameters demonstrate such changes. Since the research by the authors of the present article confirmed these interdependencies, a Recommender System to Analyze Student’s Academic Performance (Recommender System hereafter) has been developed. The Recommender System determines the level of learning productivity integrally by employing three main techniques (physiological, psychological and behavioral). This Recommender System, developed by these authors, uses motivational, educational persistence and social learning theories and the database of best global practices based on above theories to come up with recommendations for students on how to improve their learning efficiency. The Recommender System can pick learning materials taking into account a student’s learning productivity and the degree to which learning is interesting. Worldwide research includes various scientists who conducted in-depth studies on the different and very important areas of physiological measurements and intelligent systems. We did not manage to find any physiological measurements or any intelligent or integrated system that would take physiological parameters of students, analyze their learning efficiency and, in turn, provide recommendations.     

Facilitating application of language skills in authentic environments with a mobile learning system

We uncovered two critical issues in earlier studies: (a) some studies have shown that mobile learning technology is not beneficial for all students due to complexity of learning environments and student prior knowledge, skills, and experience and (b) familiarity of students with the authentic environments in which they learn using mobile technology did not receive much attention in earlier studies. To address these issues, we designed three learning tasks for a class of 26 junior high school students. The students applied language skills by completing the tasks in authentic environments individually in a first task, loosely collaborating with peers in a second task, and tightly collaborating with peers in a third task. A mobile learning system was also designed in this study to support students to accomplish the tasks. The aim of this study was to explore students' learning experiences using the learning system, their perceptions towards the system, and to assess how differently the students perform on the three tasks. According to our design, in the first task, the students took pictures of objects and described them orally or in writing using the mobile learning system. In the second task, after the students completed assignments, each student received comments from a partner through the system. In comments, the partner indicated flaws in student assignments and suggested how to fix them. In the third task, the students completed assignments, shared them using the system, and then exchanged comments with their partners face to face regarding issues related to their completed assignments and suggested how to improve them. Such learning behaviours in the three tasks enabled the students to practise writing and speaking skills. Our results demonstrate that most of the students highly valued our learning system and intend to use it in the future. Furthermore, the results show that the students performed best when they collaborated; namely, student performance was enhanced the most after the third task that required tight collaboration. Based on our results, we learned that students' familiarity with authentic environments is very important and beneficial for their learning. In addition, we learned that even in complex environment, less skilled and experienced students with low prior knowledge can perform well when they tightly collaborate with more skilled and experienced students with high prior knowledge, and our learning system can facilitate such collaboration.     

Case-based teaching using the Laboratory Animal System for learning C/C++ programming

Teachers and students often consider learning programming a difficult pursuit. Face-to-face learning alone cannot provide effective teaching or efficient solutions for learning. A case teaching model can make students active in programming courses, even as it relies on solid learning theory and pedagogical strategies. This paper reports a study based on a case teaching model in C/C++ programming. The Laboratory Animal System (LAS) is a standalone case for management of laboratory animals. This paper includes an overview of LAS architectural design and user interface by C/C++ and presents the design, implementation, and evaluation of the model, as well as its implications for learning computer programming. The case method provides an interactive learning environment for students. Based on a survey of student feedback, students can learn C/C++ programming and gain knowledge more quickly and effectively using the case teaching model than through some traditional methods of teaching.     

《操作系统原理》课程自主化学习教学策略研究

操作系统课程是一门偏理论性的课程,课程内容所涉及的知识点错综复杂,在庞大的计算机系统知识环节中充当着重要一环,学生在学习这类原理型课程的过程中往往很被动,这样很难对操作系统的课程有全局性较透彻的理解和领悟。从分析学生学习原理型课程的现状出发,对促进学生提高自主化学习操作系统原理的策略展开了研究。     

Using Evaluation to Shape ITS Design: Results and Experiences with SQL-Tutor

This paper presents the results of three evaluation studies performed during 1998 and 1999 on SQL-Tutor, an intelligent tutoring system for the SQL database language. We have evaluated the system in the context of genuine courses, and used the results to further refine the system. The main goal of our research has been the exploration and extension of Constraint-Based Modeling (CBM), a student modeling approach proposed by Ohlsson (1994). SQL-Tutor provided us with experiences of using CBM, and we used it to extend the approach in several important ways. The main goal of all three evaluation studies was to determine how well CBM supported student learning. We have obtained positive results. The students who learnt with SQL-Tutor in the first study performed significantly better than those who did not when assessed by a subsequent classroom examination. Furthermore, the analysis of students' learning shows that CBM has a sound psychological foundation.Besides the evaluation of CBM, we also evaluated the improvements in terms of student assessments of the usefulness of the system and evaluated various techniques used in SQL-Tutor. In the second study, we evaluated the effectiveness of feedback provided to the students. This study showed that high-level advice is most beneficial to students' learning. The focus of the third study was different. We extended CBM to support long-term modeling of student knowledge, and used this extension to develop an adaptive problem-selection strategy. The study revealed the benefits of this strategy in comparison with a simple heuristic strategy. We also reflect on our experiences in evaluating SQL-Tutor.     

贝叶斯网在在线测试系统学生模型中的应用研究

文章基于贝叶斯网络理论,提出了知识覆盖型贝叶斯网学生模型。文章深入探讨了构建贝叶斯网学生模型的方法和途径,并实现了自适应选题算法和推荐引擎算法,使模型具有很强的推理能力。把模型用于在线测试中,能自适应选题,自动结束,结果除获得成绩外,还能诊断,并把测试中未掌握的知识项呈现出来。从而,为准确掌握学生的知识水平,为教学策略的调整和学生进一步学习提供了有力依据。实验表明该算法能高效快捷地将学生测试中未掌握好的知识项产生推荐集合。     

Collaboration factors,teamwork satisfaction,and student attitudes toward online collaborative learning

This study examined online courses with collaborative learning components from 197 graduate students across three consecutive academic years. A student attitude survey containing 20 items and a student teamwork satisfaction scale containing 10 items on a 5-point Likert-type scale with three open-ended questions regarding their online collaborating experiences were collected during the final week of each semester. Results revealed that the three extracted online collaboration factors (Team Dynamics, Team Acquaintance, and Instructor Support) from the student attitude survey had moderate to high degrees of correlation with teamwork satisfaction. Results also revealed that the three collaboration factors accounted for 53% of the variance in online teamwork satisfaction. In addition, results from both surveys and open-ended questions revealed students favored working collaboratively in an online environment.     

Interactive remote instruction: lessons learned

This paper describes experiences with the use of the interactive remote instruction (IRI) system which was used to teach a computer science graduate course at Old Dominion University during the Fall 1995 semester. Through the use of high-speed networks and high performance workstations, IRI creates a virtual classroom so that geographically dispersed students can fully participate in a class. Central to this system is the availability of a personal workstation to each student through which the class is taken. Placing a workstation at each student's disposal opens the opportunity for student interaction and group collaboration. We compare this approach with other educational settings and describe lessons learned from instructor, student and technical perspectives. We also describe changes we are putting in place for an expanded usage in the coming fall semester using fiberoptic cable supporting a WAN internet protocol.     

It's not about seat time: Blending,flipping, and efficiency in active learning classrooms

This study examines the effect of reducing the seat time of a large lecture chemistry class by two-thirds and conducting it in an active learning classroom rather than a traditional amphitheater. To account for the reduced lecture, didactic content was recorded and posted online for viewing outside of the classroom. A second experimental section, also in a blended and flipped format, was examined the following semester as a replication. To measure student subject-matter learning, we used a standardized multiple-choice exam, and to measure student perceptions of the classroom, we used a validated survey instrument. Our findings demonstrated that in an active learning classroom, student faculty contact could be reduced by two-thirds and students achieved learning outcomes that were at least as good, and in one comparison significantly better than, those in a traditional classroom. Concurrently, student perceptions of the learning environment were improved. This suggests that pedagogically speaking, active learning classrooms, though they seat fewer students per square foot, are actually a more efficient use of physical space.     

Accumulating and visualising tacit knowledge of teachers on educational assessments

Assessments, embedded with teachers’ implicit (i.e. tacit) domain knowledge, play an important role in evaluating ’comprehension of a subject. The knowledge on the importance of both the concepts and their relationships of a subject, if captured, made explicit, and shared around, may greatly help teachers construct more effective assessments. This study establishes a methodology to accumulate tacit knowledge of specific topics from collected assessments by using an implicit knowledge extraction mechanism and, visualises the overall importance distribution of concepts by using knowledge maps for helping teachers compile their assessments. Several two stage experiments, scheduled for one semester, were conducted in the third grade natural science courses at elementary schools in Taiwan. Eighteen teachers who actually teach the courses participated in the experiments, and thirty students were in each course. In the first stage, teachers compiled assessments without using IKMAAS’s knowledge map features while in the second stage, they did use them. System usage records, questionnaires and interview results were used for evaluating the usability of the methodology and the satisfaction of using IKMAAS. The results indicate the potential of the methodology, as each of the teachers agreed that the visualised assessment knowledge helped them to comprehend the proportions of concepts they intended to test easily and, additionally, helped them to clearly notice concepts they may have ignored. Yet the results in this study also show the potential of using knowledge maps and knowledge accumulating methodology in pedagogy paradigm.     

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.