Closing the Loop in Feedback Driven Learning Environments Using Trust Decision Making and Utility Theory

Kanwal Mahmud, MUHAMMAD USMAN, Muddassar Azam Sindhu, Alireza Jolfaei, Gautam Srivastava

Research output: Contribution to journalArticle (journal)peer-review

3 Citations (Scopus)


Contemporary learning systems are an integration of learning resources with human interactions. To close the loop in feedback driven learning environments, the utility of learning objectives needs to be measured. To this end, a comprehensive trust evaluation model for decision making is required to utilize feedback ratings along with other key parameters such as previous course result percentage, active participation and reputation of learners. This paper proposes a novel utility theory based trust evaluation model, wherein the utility of a learning objective is computed in terms of trust applicable to big data-sets. The utility is computed by allowing users to weigh the course related attributes according to their preferences. The utility value facilitates learners to select trustworthy learning objectives and enables instructors to improve different aspects of learning objectives. In addition, a satisfaction index is proposed for the assessment of the usefulness of the computed utility value. The performance of the model is evaluated on a big data-set, which is collected from learners enrolled in different courses of a postgraduate degree program for the purposes of decision making. The results indicate that the proposed unique intelligent model is effective for dynamic and user-specified trust evaluations of learning objectives for the purposes of decision making.
Original languageEnglish
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Issue number1
Early online date20 May 2020
Publication statusPublished - 1 Feb 2021


  • big data
  • decision making
  • educational cyber physical systems
  • feedback loop
  • learning systems
  • trust
  • unique environmnents
  • utility theory


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