A machine learning enabled affective E-learning system model

Liu, Xinyang; Ardakani, Saeid Pourroostaei

doi:10.1007/s10639-022-11010-x

Cited by 14 publications

(9 citation statements)

References 41 publications

(36 reference statements)

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“…The results suggest that tailoring learning contents based on students' affective states resulted to increased engagement in learning activities and promoted learning. Liu and Ardakani ( 2022 ) collected students' brainwave data using portable electroencephalogram and applied k-nearest neighbor (KNN) machine learning algorithm to recognize affective states in real-time. Based on the recognized states, learning contents were automatically recommended to students.…”

Section: Discussionmentioning

confidence: 99%

“…In order to enhance teaching and learning, a variety of studies discovered and investigated different student characteristics to identify their influence on learning progress and academic performance (Arroyo et al, 2014 ; Halawa et al, 2016 ; Liu and Ardakani, 2022 ). Some of the characteristics include student learning motivation, engagement, affective states, etc.…”

Section: Introductionmentioning

confidence: 99%

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Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review

Orji

Vassileva²

2022

Front. Artif. Intell.

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Student characteristics affect their willingness and ability to acquire new knowledge. Assessing and identifying the effects of student characteristics is important for online educational systems. Machine learning (ML) is becoming significant in utilizing learning data for student modeling, decision support systems, adaptive systems, and evaluation systems. The growing need for dynamic assessment of student characteristics in online educational systems has led to application of machine learning methods in modeling the characteristics. Being able to automatically model student characteristics during learning processes is essential for dynamic and continuous adaptation of teaching and learning to each student's needs. This paper provides a review of 8 years (from 2015 to 2022) of literature on the application of machine learning methods for automatic modeling of various student characteristics. The review found six student characteristics that can be modeled automatically and highlighted the data types, collection methods, and machine learning techniques used to model them. Researchers, educators, and online educational systems designers will benefit from this study as it could be used as a guide for decision-making when creating student models for adaptive educational systems. Such systems can detect students' needs during the learning process and adapt the learning interventions based on the detected needs. Moreover, the study revealed the progress made in the application of machine learning for automatic modeling of student characteristics and suggested new future research directions for the field. Therefore, machine learning researchers could benefit from this study as they can further advance this area by investigating new, unexplored techniques and find new ways to improve the accuracy of the created student models.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review

Orji

Vassileva²

2022

Front. Artif. Intell.

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“…De-Arteaga et al [19] stated that the growth of the IoT allows for connections to be made using digital devices for data transmission, which improves online platforms. Huang et al and Liu and Ardakani [11,14] suggested that IoT speeds up the process of student and teacher participation in digital platforms through the use of emerging digital technologies. Therefore, our study disputes that digital platforms are largely based on the sound mechanisms of organizational IoT.…”

Section: Iot and Depsmentioning

confidence: 99%

“…On the other hand, Gillet et al [13] stated that DEPs improve the students' and learners' attachment to the learning processes of educational institutions, as well as enable the students to accept the changes made by the institutions in their learning processes. Liu and Ardakani [14] documented that when students are highly attached to the institution regarding the execution of digital educational activities, they are more inclined to record higher machine learning adoption. Existing studies such as Almaiah et al and Huang and Li [5,11] have highlighted that educational institutions with IoT provide massive opportunities for the involvement of students in their strategic learning decisions.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Adoption in Educational Institutions: Role of Internet of Things and Digital Educational Platforms

Wang

2023

Sustainability

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The ever-increasing development of information technologies has led to the adoption of advanced learning techniques. In this regard, e-learning and machine learning are two of the emerging instructional means for educational institutes. The current study investigates the role of the Internet of Things (IoT) and digital educational platforms (DEPs) in the adoption of machine learning. The present research additionally investigated the function of DEPs as mediators between IoT and machine learning adoption. The department chairs or heads of 310 departments at 91 Chinese institutions provided the information. In order to analyze the data, we used SPSS 25.0 and SEM (structural equation modeling). The results demonstrated how crucial an impact IoT has on DEPs and the uptake of machine learning. DEPs directly affect machine learning adoption and also act as mediators. The findings also support the mediating role of DEPs in the IoT and machine learning adoption link. The current study contributes to both theory and practical management by examining how IoT is helpful for achieving machine learning adoption. Based on the responses of 91 educational departments, this is a unique study of the mechanisms to achieve machine learning adoption through IoT and DEPs.

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“…Assess students and grade them using ML model which creates automated adaptive assessments ( Liu & Ardakani, 2022 ). This model will provide continuous feedback to the teachers about the student’s learning ability and their progress towards learning goals.…”

Section: Introductionmentioning

confidence: 99%

Research on quality evaluation of innovation and entrepreneurship education for college students based on random forest algorithm and logistic regression model

Lü¹,

Chai

Ren

et al. 2023

PeerJ Computer Science

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The quality evaluation of innovation and entrepreneurship (I&E) in the education sector is achieving worldwide attention as empowering nations with high quality talents is quintessential for economic progress. China, a pioneer in the world market in almost all sectors have transformed its educational policies and incorporated entrepreneurial skills as a part of their education models to further catalyst the country’s economic progress. This research focuses on building a novel hybrid Machine Learning (ML) model by integrating two powerful algorithms namely Random Forest (RF) and Logistic Regression (LR) to assess the intensity of the I&E in education from the data acquired from 25 leading Higher Educational Institution’s (HEI) in different provinces. The major contributions to the work are, (1) construction of quality index for each topic of interest using individual RF, (2) ranking the indicators based on the quality index to assess the strength and weaknesses, (3) and finally use the LR algorithm study the quality of each indicator. The efficacy of the proposed hybrid model is validated using the benchmark classification metrics to assess its learning and prediction performance in evaluating the quality of I&E education. The result of the research portrays that the universities have now started to integrate entrepreneurship skills as a part of the curriculum, which is evident from the better ranking of the topic curriculum development which is followed by the enrichment of skills. This comprehensive research will help the institutions to identify the potential areas of growth to boost the economic development and improve the skill set necessary for I&E education among college students.

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A machine learning enabled affective E-learning system model

Cited by 14 publications

References 41 publications

Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review

Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review

Machine Learning Adoption in Educational Institutions: Role of Internet of Things and Digital Educational Platforms

Research on quality evaluation of innovation and entrepreneurship education for college students based on random forest algorithm and logistic regression model

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