A conversational intelligent tutoring system to automatically predict learning styles

Latham, Annabel; Crockett, Keeley; McLean, David; Edmonds, Bruce

doi:10.1016/j.compedu.2011.11.001

Cited by 136 publications

(130 citation statements)

References 23 publications

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“…The factor of Learning Data is retrieved in the SCI. Some researchers indicated that smart classroom can give adaptive learning support to the student by detecting, recording, and analyzing the students' entire learning statuses (Dogan and Camurcu 2007;Zhang et al 2009;Latham et al 2012). Students' learning data is the basis for constructing learning model and giving the individualized learning diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of the smart classroom inventory

Kong

Guang

2015

Smart Learn. Environ.

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This paper discusses the development and validation of the Smart Classroom Inventory (SCI). The SCI is derived from existing technology integration learning environment instruments, including TROFLEI, TICI and CCEI (ergonomic). To accurately describe the features of smart classroom, the factors of flexibility, learning data, learning experience are added to SCI, and the description of information technology usage of some items are also altered to fit the smart classroom environment. More than 640 11 to 15 years old students validated the instrument, revealing ten scales: Physical Design, Flexibility, Technology Usage, Learning Data, Differentiation, Investigation, Cooperation, Students Cohesiveness, Equity, and Learning Experience. Cronbach's alpha coefficient is 0.92 and 0.94 for the entire questionnaire in Actual and Preferred forms respectively. The results of EFA and CFA prove SCI to be a parsimonious instrument for assessing the smart classroom learning environment. In assessing Actual and Preferred learning environments, SCI results indicated that there are significant differences in all of the ten factors. Students' perception of factors of Differentiation, Flexibility, Technology Usage, and Learning Data are the largest four actual-preferred discrepancies within the smart classroom learning environment. These suggest the importance of innovative application of the current information equipment, and schools should pay more attention to satisfying the students' personalized learning demands. The differences between the gender and between the school settings indicate that smart classroom should integrate both the technology and the humanities factors into the design and application of smart classroom, as well as design multi-level learning activities to satisfy the choices of different age students.

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Section: Discussionmentioning

confidence: 99%

Development and validation of the smart classroom inventory

Kong

Guang

2015

Smart Learn. Environ.

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“…For this study, this learning style model was used because learning objects were presented to students in an online environment. Felder and Silverman's learning style model [13] determines the learning style of an individual by scoring the nature and power of his/her learning preferences in four dimensions (perception, input, processing and understanding) [30]. The perception dimension describes the relationship of a student with the information type he/she prefers perceiving; the processing dimension describes the conversion pattern of the perceived information to understanding; the input dimension describes the preference pattern of the students to receive external information; and the understanding dimension describes the student' understanding processes [15].…”

Section: Learning Stylesmentioning

confidence: 99%

The Analysis of the Relationship between Primary Learning Styles and Learning Objects in an Online Environment

Özdemir

2016

European Journal of Contemporary Education

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This study investigates the relationships between the primary learning styles of students and different learning objects presented simultaneously in an online learning environment in the context of the usage levels of these objects. A total of 103 sophomores from a Turkish State University participated in the study. Felder-Solomon Index of Learning-Styles (F-SILS) was used to determine the learning styles of the participants. Four different types of learning objects (i.e. video lecturing, audio lecturing, PDF lecturing and subject comprehension tests) were prepared for the course ‗Basic database operations with MySQL'. Koper's (2003) classification model was used in selecting these learning objects. Descriptive analysis methods were used to determine the distribution of the participants according to their learning styles. Independent-Samples T-Test and the Mann-Whitney Wilcoxon test were used to test the differences between learning styles and learning objects. The usage levels of the learning objects were analysed in the context of interdimensional primary learning styles in the scale of the F-SILS. Those with sensory and visual learning styles were in the majority among the primary learning styles of participants. The study did not include the findings of students with other primary learning styles due to their small sample size. The findings of the study on the usage frequencies of subject comprehension tests and the duration of video lectures by primarily visual and sensory students demonstrated a significant difference on behalf of the primary sensory students. On the other hand, there was no statistically significant difference between students with primarily sensory styles and students with primarily visual styles in terms of the reading frequency of PDF lectures and the listening frequency of audio lectures.

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“…They find the Markov based approach leads to more fine-tuned results than the Genetic Algorithm. Latham, Crockett, McLean, and Edmonds (2012) predict students' learning style through natural language dialogue. In this respect, they analyze the learning style models for language traits and conversational structure and adapt the generic logic rules to predict learning styles.…”

Section: Learning Style Modelingmentioning

confidence: 99%

Tuning Primary Learning Style for Children with Secondary Behavioral Patterns

Mosharraf

2016

IJELL

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Personalization is one of the most expected features in the current educational systems. User modeling is supposed to be the first stage of this process, which may incorporate learning style as an important part of the model. Learning style, which is a non-stable characteristic in the case of children, differentiates students in learning preferences. This paper identifies a new hybrid method to initiate and update the information of children's learning style in an educational system. At the start-up phase, children's learning style information is gathered through the modified Murphy-Meisgeier Type Indicator for Children (MMTIC) questionnaire, which is based on the wellknown Myers-Briggs Type Indicator (MBTI). This primary information will be tuned by tracking children's behaviors during the learning process. Analytical data mining helped us to cluster these behaviors and find their patterns. The proposed method was applied on 81 fourth grade children in elementary school. Delivering results suggest that this method provides a good precision in recognizing children learning style and may be an appropriate solution for non-stability problems in their preferences.

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A conversational intelligent tutoring system to automatically predict learning styles

Cited by 136 publications

References 23 publications

Development and validation of the smart classroom inventory

Development and validation of the smart classroom inventory

The Analysis of the Relationship between Primary Learning Styles and Learning Objects in an Online Environment

Tuning Primary Learning Style for Children with Secondary Behavioral Patterns

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