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Computer Science

Adaptive Smart TV as a Social Language Learning Platform

Profile image of María José Naranjo SánchezMaría José Naranjo Sánchez

2019, Proceedings of the 16th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2019)

https://doi.org/10.33965/CELDA2019_201911L008
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Abstract

With the recent emergence of the interactive TV, the iTV starts to gain ground as a learning social media forgotten in the last decades due to previously existing applications (smart phones, tablets, laptops) that surpassed it. The TV has always been used as a means of transmission and family conciliatory, but, at the same time, the necessary feedback, as well as its adaptation to the users' preferences, language level, and age have always been lacking in every learning process that smart televisions can currently provide. In this sense, it seems necessary, on the one hand, to investigate the different characteristics and capabilities of smart televisions and, on the other, to design educational learning pills for English language learning through adaptive smart TVs. To achieve the aforementioned objectives an interactive methodology based on family/ peer gaming will be presented as a way to awake collective and social learning. The expected results will be an application developed with Hybrid Broadcast Broadband technology including a content recommendation system designed considering the user's preferences and a set of educational pills created by a team of linguists and experts in language and methodology. To conclude, this project seeks to promote language learning acquisition in a fun and entertaining way and enhance the experience with their television which has been a traditional model representing bond and time to gather around family and friends.

Figures (7)
Figure 1. Structure of the STVALL project As a support of this research, the STVALL project aims, on the one hand, to develop an educational platform for personalized interactive teaching of languages through iTV. The architecture of the platform (see figure 1) is divided into two distinct blocks: (1) the user (client part) that interacts with the intelligent system, and (2) the server where the educational content and individual user profiles are stored, and where the adaptation engine and the author web tool for the construction of training pills are resided. The adaptation engine comprises the recommendation motor and the evaluation system.
Figure 1. Structure of the STVALL project As a support of this research, the STVALL project aims, on the one hand, to develop an educational platform for personalized interactive teaching of languages through iTV. The architecture of the platform (see figure 1) is divided into two distinct blocks: (1) the user (client part) that interacts with the intelligent system, and (2) the server where the educational content and individual user profiles are stored, and where the adaptation engine and the author web tool for the construction of training pills are resided. The adaptation engine comprises the recommendation motor and the evaluation system.
for the tasks and the ones already done, correct and incorrect answers, attempts of each assignment, score obtained and the percentage of exercises done applied for the skills listening, writing and reading will be recorded. These data allow the recommendation system to work effectively. This system is used to recommend appropriate learning materials to learners based on the learner’s profile and also the work carried out by each individual, providing personalization based on their current situation and experience. Additionally, linguists and experts in language and methodology can access the platform to create activities, modify and delete tasks that were previously created. Pending activities in need of feedback will be also corrected and commented by experts.
for the tasks and the ones already done, correct and incorrect answers, attempts of each assignment, score obtained and the percentage of exercises done applied for the skills listening, writing and reading will be recorded. These data allow the recommendation system to work effectively. This system is used to recommend appropriate learning materials to learners based on the learner’s profile and also the work carried out by each individual, providing personalization based on their current situation and experience. Additionally, linguists and experts in language and methodology can access the platform to create activities, modify and delete tasks that were previously created. Pending activities in need of feedback will be also corrected and commented by experts.
members. Each participant plays in their turn adding points provided correct answers. The user who first reaches the maximum score will be the winner of the game. Once finished, the engine stores the progress of each registered user, adding the training pills made to their history and adding the points won to their total score. 4. RESULTS: LANGUAGE LEARNING DESIGN THROUGH ADAPTIVE ITV
members. Each participant plays in their turn adding points provided correct answers. The user who first reaches the maximum score will be the winner of the game. Once finished, the engine stores the progress of each registered user, adding the training pills made to their history and adding the points won to their total score. 4. RESULTS: LANGUAGE LEARNING DESIGN THROUGH ADAPTIVE ITV
Figure 5. Interactive activity with immediate feedback e Interactive or evaluation with immediate feedback tasks, where the student must provide some answer to the question arisen. The feedback can be offered by the evaluation engine without the intervention of the expert. When presenting speaking and writing activities, the feedback can be given by peers, promoting self-evaluation or by experts.
Figure 5. Interactive activity with immediate feedback e Interactive or evaluation with immediate feedback tasks, where the student must provide some answer to the question arisen. The feedback can be offered by the evaluation engine without the intervention of the expert. When presenting speaking and writing activities, the feedback can be given by peers, promoting self-evaluation or by experts.
Figure 6. Interactive activity with pre-recorded feedback
Figure 6. Interactive activity with pre-recorded feedback
Figure 4. Non-interactive activity
Figure 4. Non-interactive activity
Figure 7. Different types of activities designed After the review of literature about the strengths and weaknesses of mobile applications in comparison to the iTV, focusing on their trends, challenges and opportunities and our depiction of how the teaching and learning of English can effectively work into Television leisure and education, we hope to offer qualitative and quantitative results about the learning improvement of the sample that will be tested in short. Similar research justifies the learning improvement of t-learning applications developed (Dos Santos, D.T. et al, 2006; Souza, Maicon & Bizelli, José, 2012).
Figure 7. Different types of activities designed After the review of literature about the strengths and weaknesses of mobile applications in comparison to the iTV, focusing on their trends, challenges and opportunities and our depiction of how the teaching and learning of English can effectively work into Television leisure and education, we hope to offer qualitative and quantitative results about the learning improvement of the sample that will be tested in short. Similar research justifies the learning improvement of t-learning applications developed (Dos Santos, D.T. et al, 2006; Souza, Maicon & Bizelli, José, 2012).

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References (13)

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