An Optimization-Based Artificial Intelligence Framework for Teaching English at College Level Under Tribhuvan University

Learning and computing breakthroughs among students are beginning to converge due to the rapid growth of digital technology. Artificial Intelligence (AI) has made an impact on the way we teach English at college level. It has an enormous potential of providing digitalized and completely personalized learning to each English language teacher. This quantitative quasi-experimental research offers a strategy for incorporating Artificial Intelligence (AI) in English language teaching at college level. The participants consisted of 100 bachelor level students studying at a constituent college of Tribhuvan University, Nepal. The participants were selected using simple random sampling and divided into two groups: the study group and the control group. The researcher employed questionnaire and test as the instruments to collect the data. The collected data was analyzed using SPSS 2.0 which is a tool for analyzing quantitatively challenging data. The findings were presented descriptively and the researcher assessed the model's criteria, designed a comparison test, and conducted a survey questionnaire to check the reliability and effectiveness of the prediction. The evidence shows that Enhanced Whale Hyper-Tuned Artificial Neural Network (EWH-ANN) EWH-ANN can be employed to optimize English instruction at college level in general and verbal improvement in particular. It can make English teaching more efficient and customized to fulfil individual students' necessities. The study concluded that The Whale Optimization Algorithm (WOA) can be used to tune the hyper-parameters of Artificial Neural Network (ANN) to improve the accuracy of the operation

Chatbots for learning: A review of educational chatbots for the Facebook Messenger

With the exponential growth in the mobile device market over the last decade, chatbots are becoming an increasingly popular option to interact with users, and their popularity and adoption are rapidly spreading. These mobile devices change the way we communicate and allow ever-present learning in various environments. This study examined educational chatbots for Facebook Messenger to support learning. The independent web directory was screened to assess chatbots for this study resulting in the identification of 89 unique chatbots. Each chatbot was classified by language, subject matter and developer's platform. Finally, we evaluated 47 educational chatbots using the Facebook Messenger platform based on the analytic hierarchy process against the quality attributes of teaching, humanity, affect, and accessibility. We found that educational chatbots on the Facebook Messenger platform vary from the basic level of sending personalized messages to recommending learning content. Results show that chatbots which are part of the instant messaging application are still in its early stages to become artificial intelligence teaching assistants. The findings provide tips for teachers to integrate chatbots into classroom practice and advice what types of chatbots they can try out.Web of Science151art. no. 10386

Enriching Information Technology Course Materials by Using Youtube

IT offers some benefits and collaborations in various sectors. This research focuses on exploring higher education subjects via social technology, YouTube. YouTube is the world largest video based contents application in the world. Current learning materials are not only in text and images, but included video contents. This research enriching students learning materials may involving YouTube as learning sources. The study observed 118 sophomore students in computer science faculty. The results show that, involving YouTube in enriching students course material able to create conductive learning environment. This strategy increases students understanding in their field of study.Comment: Excellent Paper Award of AICSIT2017, 8 page

Revisión tecnológica del aprendizaje de idiomas asistido por ordenador: una perspectiva cronológica

El presente artículo aborda la evolución y el avance de las tecnologías del aprendizaje de lenguas asistido por ordenador (CALL por sus siglas en inglés, que corresponden a Computer- Assisted Language Learning) desde una perspectiva histórica. Esta revisión de la literatura sobre tecnologías del aprendizaje de lenguas asistido por ordenador comienza con la definición del concepto de CALL y otros términos relacionados, entre los que podemos destacar CAI, CAL, CALI, CALICO, CALT, CAT, CBT, CMC o CMI, para posteriormente analizar las primeras iniciativas de implementación del aprendizaje de lenguas asistido por ordenador en las décadas de 1950 y 1960, avanzando posteriormente a las décadas de las computadoras centrales y las microcomputadoras. En última instancia, se revisan las tecnologías emergentes en el siglo XXI, especialmente tras la irrupción de Internet, donde se presentan el impacto del e-learning, b-learning, las tecnologías de la Web 2.0, las redes sociales e incluso el aprendizaje de lenguas asistido por robots.The main focus of this paper is on the advancement of technologies in Computer-Assisted Language Learning (CALL) from a historical perspective. The review starts by defining CALL and its related terminology, highlighting the first CALL attempts in 1950s and 1960s, and then moving to other decades of mainframes and microcomputers. At the final step, emerging technologies in 21st century will be reviewed

Adaptive hypermedia for education and training

Adaptive hypermedia (AH) is an alternative to the traditional, one-size-fits-all approach in the development of hypermedia systems. AH systems build a model of the goals, preferences, and knowledge of each individual user; this model is used throughout the interaction with the user to adapt to the needs of that particular user (Brusilovsky, 1996b). For example, a student in an adaptive educational hypermedia system will be given a presentation that is adapted specifically to his or her knowledge of the subject (De Bra & Calvi, 1998; Hothi, Hall, & Sly, 2000) as well as a suggested set of the most relevant links to proceed further (Brusilovsky, Eklund, & Schwarz, 1998; Kavcic, 2004). An adaptive electronic encyclopedia will personalize the content of an article to augment the user's existing knowledge and interests (Bontcheva & Wilks, 2005; Milosavljevic, 1997). A museum guide will adapt the presentation about every visited object to the user's individual path through the museum (Oberlander et al., 1998; Stock et al., 2007). Adaptive hypermedia belongs to the class of user-adaptive systems (Schneider-Hufschmidt, Kühme, & Malinowski, 1993). A distinctive feature of an adaptive system is an explicit user model that represents user knowledge, goals, and interests, as well as other features that enable the system to adapt to different users with their own specific set of goals. An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user. The user model is applied to provide an adaptation effect, that is, tailor interaction to different users in the same context. In different kinds of adaptive systems, adaptation effects could vary greatly. In AH systems, it is limited to three major adaptation technologies: adaptive content selection, adaptive navigation support, and adaptive presentation. The first of these three technologies comes from the fields of adaptive information retrieval (IR) and intelligent tutoring systems (ITS). When the user searches for information, the system adaptively selects and prioritizes the most relevant items (Brajnik, Guida, & Tasso, 1987; Brusilovsky, 1992b)

Using motivation derived from computer gaming in the context of computer based instruction

This paper was originally presented at the IEEE Technically Sponsored SAI Computing Conference 2016, London, 13-15 July 2016. Abstract— this paper explores how to exploit game based motivation as a way to promote engagement in computer-based instruction, and in particular in online learning interaction. The paper explores the human psychology of gaming and how this can be applied to learning, the computer mechanics of media presentation, affordances and possibilities, and the emerging interaction of playing games and how this itself can provide a pedagogical scaffolding to learning. In doing so the paper focuses on four aspects of Game Based Motivation and how it may be used; (i) the game player’s perception; (ii) the game designers’ model of how to motivate; (iii) team aspects and social interaction as a motivating factor; (iv) psychological models of motivation. This includes the increasing social nature of computer interaction. The paper concludes with a manifesto for exploiting game based motivation in learning