A Model for Personalized Course Material Generation Based on Student Learning Abilities and Interests

Leung, Elvis Wai Chung; Li, Qing

doi:10.1007/11925293_3

Cited by 7 publications

(7 citation statements)

References 5 publications

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“…Although there is not yet any widespread practical application of the research results, technologies involving artificial intelligent, educational theory, and statistics are good candidates in implementing automatic systems to guide students through different learning paths within the same learning content. For example, remedial courses and personalized materials can be generated [Leung and Li 2006] for each student if necessary. In addition, adaptive testing materials can be generated depending on learning performance.…”

Section: Future Directionsmentioning

confidence: 99%

Technology supports for distributed and collaborative learning over the internet

Lau²,

Shih

et al. 2008

ACM Trans. Internet Technol.

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With the advent of Internet and World Wide Web (WWW) technologies, distance education (elearning or Web-based learning) has enabled a new era of education. There are a number of issues that have significant impact on distance education, including those from educational, sociological, and psychological perspectives. Rather than attempting to cover exhaustively all the related perspectives, in this survey article, we focus on the technological issues. A number of technology issues are discussed, including distributed learning, collaborative learning, distributed content management, mobile and situated learning, and multimodal interaction and augmented devices for e-learning. Although we have tried to include the state-of-the-art technologies and systems here, it is anticipated that many new ones will emerge in the near future. As such, we point out several emerging issues and technologies that we believe are promising, for the purpose of highlighting important directions for future research.

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Section: Future Directionsmentioning

confidence: 99%

Technology supports for distributed and collaborative learning over the internet

Lau²,

Shih

et al. 2008

ACM Trans. Internet Technol.

Self Cite

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“…This fact proved that Factor 1 and Factor 2 for university A had actually dropped. Also, some p-values less than 0.05 can be found in the rows of university B for items (2), (6), (7), (11), and (12). In this case, the average values of those evaluation items actually increased.…”

Section: The General Tendencymentioning

confidence: 97%

“…P3. 2 The mean, S xy , of the significant correlation coefficients, between the items in Factor X and those in Factor Y is calculated. P3.…”

Section: P1mentioning

confidence: 99%

“…Even in the face-to-face sessions, it is very important to understand the students' motivation states because it will enable the instructors to make very effective teaching strategies. In recent E-Learning systems, there have been a lot of improvements in the function of getting feedback from the students, and that of offering the course materials that are suitable to each student based on his/her personal profile [2]. However, there have been few systems that allow the instructors to assess the students' motivation states.…”

Section: Introductionmentioning

confidence: 99%

“…Also, they will be able to improve their environments for creation if they really understand the mechanisms of the computer application they use for creating their work by learning computer programming [4]. Processing has the following attractive features for using in the introductory programming courses: (1) Fine artwork can be created from relatively simple codes, (2) It is open source and is widely available, and (3) The syntax in Processing is similar to the one in Java, which is a widely used programming language.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing the Learners’ Motivation in the E-Learning Environments for Programming Education

Takemura

Nagumo

Huang

et al.

Lecture Notes in Computer Science

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Abstract. In this paper, a pedagogical system to visualize the students' motivation states for learning programming is proposed. This system can be used in E-Learning environments so that the instructors can assess the students' motivation states in real-time. This system is based on the Systematical Information Education Method (SIEM) assessment standard proposed by Dohi [1], which is used to assess the students' motivation to learn programming. In order to visualize the students' motivation states, the correlations between the factors in the SIEM assessment standard are mapped on the width of the arcs of the graph whose nodes are the factors in the standard. As the preliminary experiments, the introductory programming courses were offered to the students in the art design and digital design faculties at two universities. It has been found that in order to maintain or raise students' motivation it is more important to allow students to enjoy viewing the final results (artwork) of the programming than to make them strive to create more beautiful artwork.

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An Experimental Study of a Personalized Learning Environment Through Open-Source Software Tools

Leung

2007

IEEE Trans. Educ.

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A Model for Personalized Course Material Generation Based on Student Learning Abilities and Interests

Cited by 7 publications

References 5 publications

Technology supports for distributed and collaborative learning over the internet

Technology supports for distributed and collaborative learning over the internet

Assessing the Learners’ Motivation in the E-Learning Environments for Programming Education

An Experimental Study of a Personalized Learning Environment Through Open-Source Software Tools

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