Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of f
- PDF / 790,973 Bytes
- 13 Pages / 595.276 x 790.866 pts Page_size
- 58 Downloads / 140 Views
(0123456789().,-volV)(0123456789(). ,- volV)
RESEARCH ARTICLE
Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow Anis Yazidi1 Erik Arntzen3
•
Asieh Abolpour Mofrad1
•
Morten Goodwin2
•
Hugo Lewi Hammer1,4
•
Received: 31 December 2019 / Revised: 24 May 2020 / Accepted: 1 June 2020 The Author(s) 2020
Abstract An adaptive task difficulty assignment method which we reckon as balanced difficulty task finder (BDTF) is proposed in this paper. The aim is to recommend tasks to a learner using a trade-off between skills of the learner and difficulty of the tasks such that the learner experiences a state of flow during the learning. Flow is a mental state that psychologists refer to when someone is completely immersed in an activity. Flow state is a multidisciplinary field of research and has been studied not only in psychology, but also neuroscience, education, sport, and games. The idea behind this paper is to try to achieve a flow state in a similar way as Elo’s chess skill rating (Glickman in Am Chess J 3:59–102) and TrueSkill (Herbrich et al. in Advances in neural information processing systems, 2006) for matching game players, where ‘‘matched players’’ should possess similar capabilities and skills in order to maintain the level of motivation and involvement in the game. The BDTF draws analogy between choosing an appropriate opponent or appropriate game level and automatically choosing an appropriate difficulty level of a learning task. This method, as an intelligent tutoring system, could be used in a wide range of applications from online learning environments and e-learning, to learning and remembering techniques in traditional methods such as adjusting delayed matching to sample and spaced retrieval training that can be used for people with memory problems such as people with dementia. Keywords Adaptive task difficulty State of flow Intelligent tutoring system Game ranking systems Online learning Adjusting delayed matching-to-sample Computerized adaptive testing Stochastic point location
Introduction
Anis Yazidi and Asieh Abolpour Mofrad have contributed equally to this work. & Anis Yazidi [email protected] Asieh Abolpour Mofrad [email protected] 1
Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
2
Department of Computer Science, University of Agder, Kristiansand, Norway
3
Department of Behavioral Science, Oslo Metropolitan University, Oslo, Norway
4
Simula Metropolitan Center, Oslo, Norway
Attempts to achieve computer tutoring systems that are as effective as human tutors can be traced back to the earliest days of computers (Smith and Sherwood 1976). Online learning is becoming a significant driving force in today’s educational systems. The lack of faculty members is a common trend in today’s universities which makes personalized one to one teaching challenging, or practically impossible. Students may struggle to fulfill their full potential because the assigned
Data Loading...
