Modeling the “Learning Process” of the Teacher in a Tutorial-Like System Using Learning Automata

Oommen, B. John; Hashem, M. Khaled

doi:10.1109/tsmcb.2013.2238230

Cited by 15 publications

(8 citation statements)

References 43 publications

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“…The estimates are calculated at the end of each iteration of the operation. Based on this idea, various algorithms have been proposed and employed in research fields . A prominent kind is the Pursuit algorithm…”

Section: Learning Automata Theorymentioning

confidence: 99%

Estimator learning automata for feature subset selection in high‐dimensional spaces, case study: Email spam detection

Seyyedi

Minaei-Bidgoli

2018

Int J Communication

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One of the difficult challenges facing data miners is that algorithm performance degrades if the feature space contains redundant or irrelevant features. Therefore, as a critical preprocess task, dimension reduction is used to build a smaller space containing valuable features. There are 2 different approaches for dimension reduction: feature extraction and feature selection, which itself is divided into wrapper and filter approaches. In high-dimensional spaces, feature extrac-

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Section: Learning Automata Theorymentioning

confidence: 99%

Estimator learning automata for feature subset selection in high‐dimensional spaces, case study: Email spam detection

Seyyedi

Minaei-Bidgoli

2018

Int J Communication

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“…There is a substantial body of work on Learning Automata (LA) and ITSs (see, e.g. Oommen and Hashem 2013 ). In simple terms, LA is a stochastic machine attempting to find the optimal strategy from a set of actions in a random environment.…”

Section: Introductionmentioning

confidence: 99%

“…The term tutorial-like systems refers to study tutorial systems while no entity needs to be a real-life individual. Research in this field tries to model components of the system with appropriate learning models, such as LA (Oommen and Hashem 2013 ).…”

Section: Introductionmentioning

confidence: 99%

“…In a tutorial-like system, the teacher also might be stochastic and learns through the process of training (Hashem 2007 ). The design and analysis of a tutorial-like system model could involve modeling of a student (Oommen and Hashem 2009b ), modeling of a classroom of students where artificial students can interact and learn from each other as well as the teacher (Oommen and Hashem 2009a ), modeling of a (stochastic) teacher (Hashem and Oommen 2007 ), modeling the domain knowledge (Oommen and Hashem 2010 ), and modeling how teaching abilities of a teacher can be improved (Oommen and Hashem 2013 ).…”

Section: Introductionmentioning

confidence: 99%

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Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow

et al. 2020

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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.

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“…11 To date, the LA has been applied in many learning problems in different areas. [12][13][14][15][16][17]…”

Section: Introductionmentioning

confidence: 99%

Wavefront cellular learning automata

Moradabadi

Meybodi

2018

Chaos

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This paper proposes a new cellular learning automaton, called a wavefront cellular learning automaton (WCLA). The proposed WCLA has a set of learning automata mapped to a connected structure and uses this structure to propagate the state changes of the learning automata over the structure using waves. In the WCLA, after one learning automaton chooses its action, if this chosen action is different from the previous action, it can send a wave to its neighbors and activate them. Each neighbor receiving the wave is activated and must choose a new action. This structure for the WCLA is necessary in many dynamic areas such as social networks, computer networks, grid computing, and web mining. In this paper, we introduce the WCLA framework as an optimization tool with diffusion capability, study its behavior over time using ordinary differential equation solutions, and present its accuracy using expediency analysis. To show the superiority of the proposed WCLA, we compare the proposed method with some other types of cellular learning automata using two benchmark problems.

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Modeling the “Learning Process” of the Teacher in a Tutorial-Like System Using Learning Automata

Cited by 15 publications

References 43 publications

Estimator learning automata for feature subset selection in high‐dimensional spaces, case study: Email spam detection

Estimator learning automata for feature subset selection in high‐dimensional spaces, case study: Email spam detection

Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow

Wavefront cellular learning automata

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