Developing Virtual Class Room Models with Bio Inspired Algorithms for E-Learning: A Survey for Higher Technical Education for Saudi Arabia Vision 2030

Fakeeh, Khalid A.

doi:10.5120/ijais2017451709

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…The habit like taking tea or coffee makes sense at a study [47] conducted in 2017, explored the link between personality type and blood group. As per the study [48] it is concluded that there is a strong correlation between Introvert and Extravert type people with their learning styles. The Chatbot interacts with the learners to classify them into Introverts and Extraverts.…”

Section: Proposed Methodsmentioning

confidence: 72%

Bio-Inspiring Learning Style Chatbot Inventory Using Brain Computing Interface to Increase the Efficiency of E-Learning

2020

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In recent times Electronic Learning (E-Learning) and Massive Open Online Courses (MOOC) are more popular among the current generation of learners. Coursera, Edx, Simplilearn, Byjus and many other E-Learning service providers are available to deliver various courses. A recent study, in online courses, it has been found by Massachusetts Institute of Technology (MIT) that an astronomical dropout rate of about 96 per cent was found for the last five years. Educational researchers are attempting to decrease the dropout rate of E-Learning courses using various methods. Human Computer Interface (HCI) researchers are attempting to use Brain Computer Interface (BCI) to increase the efficiency of the E-Learning. Beta waves (14-30 Hz) are generated when the learners are more alert. Neil Fleming's VARK (Visual, Auditory, Read and Write and Kinesthetic) questionnaires are used by many researchers to classify the learners. Carl Jung explored that Introverts and Extraverts are the personality traits among the humans. Soomin Kim's study shows that for gathering of quantitative data, Chatbot may be a promising method. The proposed research work in this paper is to find out a correlation between Introvert and Extravert personality types and their learning styles. Initially, modified VARK questionnaires are implemented as a Chatbot to classify individuals as Introverts or Extraverts. After the classifications by the Chatbot, two minutes of visual and auditory contents are given to Introverts and Extraverts and learners' Beta brain waves are recorded and a dataset is created at an interval of one second. The dataset is validated using Machine Learning (ML) algorithms, like Naïve Bayes, N48 and Canopy. The proposed method is found to improve the accuracy of classification of learners. Bio-Inspired learning style Brain Computing Interface (BIL-BCI) framework proposed in this paper is a recommendation system to increase the accuracy of the classification among the E-Learners. INDEX TERMS E-Learning, massive open online course, VARK learning style, learning styles inventory(LSI), chatbot, classification, machine learning, introverts, extraverts, brain computer interface. I. INTRODUCTION Internet is expanding to a larger extent to reach all over the world. The average global internet speed is getting faster. Educational technologies [1] have become more viable with the advent of Internet. The higher education process has been dramatically changed by the use of latest techniques and tools. Modern technologies are increasing to have easy access for learning. Human's learning online can be changed by the E-Learning and MOOCs process. A large number of learners The associate editor coordinating the review of this manuscript and approving it for publication was Mohammad Zia Ur Rahman. can learn online at their own pace by using MOOC. Open course materials and peer feedbacks are used by the learners to interact with the courses. Around the globe, many universities are offering learners a chance to learn their relevant courses through MOOCs. E-...

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Section: Proposed Methodsmentioning

confidence: 72%

Bio-Inspiring Learning Style Chatbot Inventory Using Brain Computing Interface to Increase the Efficiency of E-Learning

2020

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“…The results concluded that the headway and integration of a DSS with the higher education might settle on a compact outlay and anticipated time that would focus on the critical issues of drafting and grasping the most appropriate decisions (Fakeeh 2015).…”

Section: ) Published In the Academic Journal Of Interdisciplinary Stu...mentioning

confidence: 99%

Effect of Management Information System (MIS) on Decision-Making in the Academic Sector

Pd¹

2023

OCEM J. Mgt Tech. Soc. Sci.

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The current era is the information era; people are running on society 5.0, where machines are integrated with humans for a healthy, comfortable, easy, and long life. In general, information is blood, and MIS is body; blood flows for life which can be compared with the flow of information on the organization as per the requirement for decision-making to grow the organization to meet its desired outcome. The overall objective of this research is to assess the effect of MIS on decision-making among academic institutions. This machine can function effectively and efficiently only with the help of data and information for humanitarian assistance for various purposes. Nowadays, every organization cannot run smoothly without the use of MIS. It signifies the dependability of decision-making on MIS. This research has attempted to identify necessary variables. They are used to evaluate the influence of management information systems on decision-support capabilities in the organization. This study also discusses the concept, attributes, characteristics, types of MIS, and the MIS model, and finally, highlights the effect of MIS in decision-making in academic institutions. At the same time, different models and figures are presented to enrich the discussion and to highlight the status of each MIS and DSS information system in an organization's decision-making process. Here fifteen related articles are incorporated to review the title to ensure the research gap and where conceptual review approach based on descriptive content analysis using long-term classroom discussion. This research will create an awareness to develop an integrated MIS among academic institutions to ensure success through rational scientific decision-making.

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“…Management-centred DSSs are commonly dealing with general aspects of higher education administration and support existing institutional processes (Kasap et al, 2020;Nieto et al, 2019a;Ploywattanawong, 2016). However, existing DSSs do not adequately address institutional strategic planning and development (Fakeeh, 2015;Nieto et al, 2019b), meaning that there is a lack of systems designed to aid TLCs or similar strategic units in making informed decisions about complex processes, such as innovations in teaching and learning or strategic planning and development (Albon et al, 2016;Atkins et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Supporting Decision-Making for Promoting Teaching and Learning Innovation

Kotorov,

Krasylnykova,

Pérez-Sanagustín

et al. 2024

Learning Analytics

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The quality of the data and the amount of correct information available is key to informed decision-making. Higher education institutions (HEIs) often employ various decision support systems (DSSs) to make better choices. However, there is a lack of systems to assist with decision-making to promote innovation in teaching and learning. In this study, we evaluate an analytic tool called PROF-XXI that supports strategic decision-making of teaching and learning centres (TLCs) by identifying their competencies in teaching and learning innovation. Through a multiple case study conducted with three Latin American universities and supported by quantitative and qualitative data, we observed how this tool is used and how it facilitates strategic decision-making. Our findings indicate that the tool is accessible, user-friendly, and effective in 1) initiating identification and systematic reflection of institutional competency levels in teaching and learning innovation, 2) enhancing understanding of strengths and weaknesses as well as identifying opportunities for innovation, 3) supporting TLCs with short- and long-term decision-making, and 4) continuously evaluating their strategies, programs, and initiatives. This research can benefit policymakers in higher education who are involved in measuring institutional competencies to improve teaching quality or in making strategic decisions related to teaching and learning innovation.

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Developing Virtual Class Room Models with Bio Inspired Algorithms for E-Learning: A Survey for Higher Technical Education for Saudi Arabia Vision 2030

Cited by 4 publications

References 5 publications

Bio-Inspiring Learning Style Chatbot Inventory Using Brain Computing Interface to Increase the Efficiency of E-Learning

Bio-Inspiring Learning Style Chatbot Inventory Using Brain Computing Interface to Increase the Efficiency of E-Learning

Effect of Management Information System (MIS) on Decision-Making in the Academic Sector

Supporting Decision-Making for Promoting Teaching and Learning Innovation

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