Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3265689.3265691acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccseConference Proceedingsconference-collections
research-article

information to Intelligence(itoI): An Adaptive and Intelligent Prototype of Knowledge Construction for College Student

Published: 28 July 2018 Publication History

Abstract

An adaptive and intelligent prototype of knowledge construction for college students, information to Intelligence(itoI), is proposed in this paper. During the process of knowledge construction, the system records all kinds of online study performance of each student, and then interdisciplinary coursework is automatically assigned to the right students. Both the student and the system improve their knowledge level and intelligence level adaptively. Through the random survey on college student of Shandong Normal University, the results show that it is useful for guiding the process of online learning.

References

[1]
U.S. Department of Education Office of Educational Technology, Understanding the Implications of Online Learning for Educational Productivity, 2012.
[2]
Allen, Elaine I. and Jeff Seaman. Grade Change: Tracking Online Education in the United States. Babson Survey Research Group and Quahog Research Group, 2014.
[3]
Lack, Kelly A. Current Status of Research on Online Learning in Postsecondary Education. Ithaka S+R 2013.
[4]
Fish, Kristine and Hyun Gu Kang. Learning Outcomes in a Stress Management Course: Online versus Face-to-Face. MERLOT Journal of Online Learning and Teaching.2014,10(2): 179--191.
[5]
Xu, Di and Shanna Smith Jaggars. Performance Gaps Between Online and Face-to-Face Courses: Differences Across Types of Students and Academic Subject Areas. The Journal of Higher Education,2014,85(5): 633--659.
[6]
Steffens, K. Competences, learning theories and MOOCs: Recent developments in lifelong learning. European Journal of Education, 2015,50(1), 41--59.
[7]
Fournier, H., Kop, R., & Durand, G. (2014). Challenges to research in MOOCs. MERLOT Journal of Online Learning and Teaching, 2014,10(1), 1--15.
[8]
Kizilcec, R. F., & Schneider, E. (2015). Motivation as a lens to understand online learners: Towards data-driven design with the OLEI scale. ACM Transactions on Computer-Human Interaction, 2015,22(2), 1--12
[9]
Kizilcec, R. F., Bailenson, J. N., & Gomez, C. J. The instructor's face in video instruction: Evidence from two large-scale field studies. Journal of Educational Psychology, 2015,107(3), 724--739.
[10]
Olga Pilli, Wilfried Admiraal, Students' Learning Outcomes in Massive Open Online Courses (MOOCs): Some Suggestions for Course Design, 2017,7(1): 46--71.
[11]
Christie, A. Constructivism and its implications for educators. http://alicechristie.com/edtech/learning/constructivism/index.htm, 2005
[12]
Yueting Chai, Chunyan Miao, Baowen Sun, Yongqing Zheng, Qingzhong Li, Crowd science and engineering: concept and research framework, International Journal of Crowd Science, 2017, 1(1): 2--8.
[13]
informaiton to Ingelligence(itoI), http://www.itoi.sd.cn:8080/itoI/

Index Terms

  1. information to Intelligence(itoI): An Adaptive and Intelligent Prototype of Knowledge Construction for College Student

      Recommendations

      Comments

      Please enable JavaScript to view thecomments powered by Disqus.

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      ICCSE'18: Proceedings of the 3rd International Conference on Crowd Science and Engineering
      July 2018
      220 pages
      ISBN:9781450365871
      DOI:10.1145/3265689
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 28 July 2018

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Adaptive e-learning system
      2. Crowd science
      3. Knowledge construction
      4. Online learning

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Conference

      ICCSE'18

      Acceptance Rates

      ICCSE'18 Paper Acceptance Rate 33 of 89 submissions, 37%;
      Overall Acceptance Rate 92 of 247 submissions, 37%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 36
        Total Downloads
      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 21 Nov 2024

      Other Metrics

      Citations

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media