Adapting Learning Activities Selection in an Intelligent Tutoring System to Affect

Odo, Chinasa

doi:10.1007/978-3-319-93846-2_98

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Manske and Hoppe [10] used a semantic al maximized the knowledge diversity in the groups. Odo et al [43] analyse dent's affective state can affect their performance in collaborative study g rentzou et al [14] and Reis [15] highlight in their work the importance of pe as critical elements that affect collaboration and students' interaction, affi Regarding their application, genetic algorithms have been used, particularly during the last two decades, in a wide range of combinatorial optimization problems, including TSP (Travel Salesman Problem), KP (Knapsack Problem), sequencing and scheduling of tasks (Scheduling), vehicle routing, among others [34][35][36][37][38][39], which makes this technique a good candidate to solve the grouping problem outlined in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…Manske and Hoppe [10] used a semantic algorithm, which maximized the knowledge diversity in the groups. Odo et al [43] analysed how the student's affective state can affect their performance in collaborative study groups. Lykourentzou et al [14] and Reis [15] highlight in their work the importance of personality traits as critical elements that affect collaboration and students' interaction, affirming that this factor can influence performance and student satisfaction, and induce various actions and behaviours in group work.…”

Section: Related Workmentioning

confidence: 99%

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Automatic Group Organization for Collaborative Learning Applying Genetic Algorithm Techniques and the Big Five Model

2021

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In this paper, an approach based on genetic algorithms is proposed to form groups in collaborative learning scenarios, considering the students’ personality traits as a criterion for grouping. This formation is carried out in two stages: In the first, the information of the students is collected from a psychometric instrument based on the Big Five personality model; whereas, in the second, this information feeds a genetic algorithm that is in charge of performing the grouping iteratively, seeking for an optimal formation. The results presented here correspond to the functional and empirical validation of the approach. It is found that the described methodology is useful to obtain groups with the desired characteristics. The specific objective is to provide a strategy that makes it possible to subsequently assess in the context what type of approach (homogeneous, heterogeneous, or mixed) is the most appropriate to organize the groups.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatic Group Organization for Collaborative Learning Applying Genetic Algorithm Techniques and the Big Five Model

2021

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“…Personality; Big 5 Robison et al, 2010;Nunes and Hu, 2012;Alhathli et al, 2016Alhathli et al, , 2018bDennis et al, 2016 Psychological aspects Self-efficacy Beal and Lee, 2005a;Mcquiggan et al, 2008 Mental effort Salden et al, 2006;Okpo et al, 2018 Motivation Beal and Lee, 2005a;Cocea and Weibelzahl, 2006 Affective state and mood Beal and Lee, 2005b;Forbes-Riley et al, 2008;Graesser et al, 2008;Odo et al, 2018 Behavior and performance Learner competence Mitrović et al, 1996;Davidovic et al, 2003;Tsiriga and Virvou, 2004;Cheng et al, 2008;Corbalan et al, 2008 Problem solving skills Melis et al, 2001;Pholo and Ngwira, 2013 Help seeking behavior and self-regulated learning Koutsojannis et al, 2001;Azevedo, 2005 Learner progress Brusilovsky et al, 1996;Revilla et al, 2008;Trotman and Handley, 2008;Verdú et al, 2012 Cognition Cognitive style Triantafillou et al, 2004;Graesser et al, 2007;Mampadi et al, 2011;Lo et al, 2012;Alhathli et al, 2018a Knowledge state and prior knowledge Shute, 1995;Ray and Belden, 2007;Kelly, 2008;Petrovica, 2013 Learning style Magoulas et al, 2003;Sun and Cheng, 2007;…”

Section: Learner Characteristics Adapted To Example Researchmentioning

confidence: 99%

Adapting Learning Activity Selection to Emotional Stability and Competence

Alhathli¹,

Masthoff²,

Beacham³

2020

Front. Artif. Intell.

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This paper investigates how humans adapt next learning activity selection (in particular the knowledge it assumes and the knowledge it teaches) to learner personality and competence to inspire an adaptive learning activity selection algorithm. First, the paper describes the investigation to produce validated materials for the main study, namely the creation and validation of learner competence statements. Next, through an empirical study, we investigate the impact on learning activity selection of learners' emotional stability and competence. Participants considered a fictional learner with a certain competence, emotional stability, recent and prior learning activities engaged in, and selected the next learning activity in terms of the knowledge it used and the knowledge it taught. Three algorithms were created to adapt the selection of learning activities' knowledge complexity to learners' personality and competence. Finally, we evaluated the algorithms through a study with teachers, resulting in an algorithm that selects learning activities with varying assumed and taught knowledge adapted to learner characteristics.

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Adapting Learning Activities Selection in an Intelligent Tutoring System to Affect

Cited by 2 publications

References 9 publications

Automatic Group Organization for Collaborative Learning Applying Genetic Algorithm Techniques and the Big Five Model

Automatic Group Organization for Collaborative Learning Applying Genetic Algorithm Techniques and the Big Five Model

Adapting Learning Activity Selection to Emotional Stability and Competence

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