A problem solving oriented intelligent tutoring system to improve students' acquisition of basic computer skills
Introduction
Information and Communication Technology course (ICT) in Chinese academy aims at developing students' comprehensive ability of computer key applications, promoting their positive attitudes, creative thinking and operational skills. However, with a large number of students in class, lengthy pieces of work or practical constraints such as time and workload, providing effective feedback and meeting individual needs of students are difficult for teachers (Buchanan, 2000, Wang, 2007). The result shows that the average of students under private tutoring was about two standard deviations above the students using traditional didactic approach and 98% students could learn better under private tutor (Bloom, 1984). Intelligent tutoring system (ITS) can provide ‘one-to-one’ individualized instruction by stimulating activities of human teachers. In our opinion, a teacher usually have to complete the following activities in teaching process: (1) explain the core knowledge of a problem; (2) show how to solve the problems with specific knowledge; (3) provide solutions and worked examples of a problem; (4) give targeted feedback to students in the process of their trying to solve the problem; (5) recommend related activities based on students' cognitive state. Student model is the core element of ITS, based on which ITS is able to select the most suitable teaching strategies, provide related examples according to the needs of students, and replace human teachers to some extent (Shi, Rodriguez, Shang, & Chen, 2002).
Currently, the research on ITS is far from enough in aspects of problem solving and the method of ‘learning by doing’ supporting. Interactive problem solving environment is still rare, especially in general construction method. Interactive model needs further investigation. Acquisition of basic computer skills is different from the theoretical knowledge learning, which cannot obtain directly from others through passive or rote learning. Therefore, we must change the traditional teaching methods and build an interactive problem solving environment to support the method of ‘learning by doing’, providing worked examples and personalized feedback.
Section snippets
Problem solving and skills acquisition
Skill as an advanced cognitive ability can be understood as the ability of using concepts and rules to solve the problem. It is difficult to be achieved by using traditional teaching methods, such as lectures, knowledge representation (Hwang, Kuo, Chen, & Ho, 2014). The learner should practice and strengthen the process continuously to complete the task. In teaching ICT, researchers gradually became aware of the importance of operational skills' training and developed a variety of teaching aids
iTutor: a problem solving oriented ITS
An effective way to acquire basic computer skills is observing the worked examples and then solving the problems in context. This concept consists two aspects, one is ‘learning from examples’, and the other is ‘learning by doing’. We designed and developed iTutor system, which is a problem solving oriented ITS. It has two advantages, (1) extend the traditional model of ITS and emphasized on tracking the process of problem solving and evaluating students' skill level; (2) build a highly
Participants
137 freshmen from four normal classes in South China Normal University and one teacher participated in the research. The teacher taught the course-‘Information and Communication Technology (ICT)’ and was experienced with iTutor and traditional web-based instruction. Most of these students had used the computer before, but their skills were varied. The four classes were assigned into two groups randomly, an experimental group and a control group. The teacher, the learning materials and the
The influence of ‘different levels of prior knowledge’ and ‘different types of teaching methods’ on student learning effectiveness
Firstly, all students were divided into three groups according to their scores of prior knowledge assessment, please see Table 1.
Before two-way ANOVA, the homogeneity of variance assumption (F5, 126 = 1.403, p > 0.05) was tested. The result indicated that the homogeneity assumption was not violated. For the results of the two-way ANOVA, please see Table 2.
Table 2 shows that both the ‘different types of teaching methods’ factor (F1, 131 = 28.844, p < 0.01) and ‘different levels of prior
Discussion and conclusions
Mimicking human teachers to implement one-to-one personalized teaching to a certain extent, is a hot but difficult spot in the research of learning environment design. Extending the traditional architecture of ITS and exploring the new method of modeling student's learning process and performance are two key issues to launch e-learning. The solution of these two issues will contribute to the launching of the e-Learning. In this paper, the author extended the traditional model of ITS, applying
Acknowledgments
This research has been partially funded by the Chinese National Education Examinations Authority Planning Project 2009KS2002 and the Natural Science Foundation in China #61305144. The authors would like to thank all the students that participated in the evaluation studies, as well as to the rest of the iTutor research team, for their efforts and contributions to the ideas in this article.
References (49)
- et al.
Learning from human tutoring
Cognitive Science
(2001) - et al.
Computer-based feedback in linear algebra: effects on transfer performance and motivation
Computers & Education
(2010) - et al.
Exploring alternative ways of assessing prior knowledge, its components and their relation to student achievement: a mathematics based case study
Studies in Educational Evaluation
(2007) - et al.
Effects of an integrated concept mapping and web-based problem-solving approach on students' learning achievements, perceptions and cognitive loads
Computers & Education
(2014) - et al.
Toward an integrated model for designing assessment systems: an analysis of the current status of computer-based assessments in science
Computers & Education
(October 2013) Multimedia learning
Psychology of Learning and Motivation
(2002)- et al.
Depressive symptoms impair everyday problem-solving ability through cognitive abilities in late life
The American Journal of Geriatric Psychiatry: Official Journal of the American Association for Geriatric Psychiatry
(2011) - et al.
Beyond intelligent tutoring systems: situations, interactions, processes and affordances
Instructional Science
(2002) - et al.
Cognitive tutors: lessons learned
The Journal of the Learning Sciences
(1995) - et al.
Incorporating partial credit in computer_aided assessment of mathematics in secondary education
British Journal of Educational Technology
(2006)
The acquisition and retention of knowledge: A cognitive view
The instructional effect of feedback in test-like events
Review of Educational Research
The Jasper project: an exploration of issues in learning and instructional design
Educational Technology Research and Development
The 2 sigma problem: the search for methods of group instruction as effective as one-to-one tutoring
Educational Researcher
The efficacy of a World-Wide Web mediated formative assessment
Journal of Computer Assisted Learning
WebgeneOS: a generative and web-based learning architecture to teach operating systems in undergraduate courses
IEEE Transactions on Education
Feedback and self-regulated learning: a theoretical synthesis
Review of Educational Research
Word-processing training and retraining: effects of adult age, experience, and interface
Psychology and Aging
RCOS: yet another teaching operating system
The Nachos instructional operating system
The impact of sequencing and prior knowledge on learning mathematics through spreadsheet applications
Educational Technology Research and Development
Implementation of motivational tactics in tutoring systems
Journal of Artificial Intelligence in Education
Prior knowledge and learning
Assessment of domain-specific and domain-transcending prior knowledge: entry assessment and the use of profile analysis
Cited by (44)
Learning recommendation with formal concept analysis for intelligent tutoring system
2020, HeliyonCitation Excerpt :It shows that mostly the previous works have applied machine learning with an expert model based on rule-based reasoning. However, the studies are limited by the fixed questions and content to suggest to users [37, 38, 39]. Fixed exercises are provided to learner with focus on learner model [32, 34, 35].
Engaging and retaining customers with AI and employee service
2020, Journal of Retailing and Consumer ServicesCitation Excerpt :Consequently, AI constitutes customers’ functional service experience. AI simulates human intelligence processes to automatically learn from experience and perform human-like tasks to improve task efficiency (Wang et al., 2015). AI has been applied in various industries, particularly in the service sector, such as the hotel industry to enhance decision making, reinvent business operations, and improve customer experience (Lu et al., 2019).
Analysis of the Effectiveness of Feedback Provision in Intelligent Tutoring Systems
2024, Communications in Computer and Information ScienceAn empirical study on adoption of artificial intelligence in human resource management
2023, Artificial Intelligence Techniques in Human Resource ManagementArtificial Intelligence and Human Resource Management in Public Sector of Bahrain
2023, Lecture Notes in Networks and SystemsThe Effect of Gamified Adaptive Intelligent Tutoring System Artibos on Problem-Solving Skills
2023, Participatory Educational Research