Evaluation of the Hybrid Pedagogic Method in Students’ Progression in Learning Using Neural Network Modelling and Prediction

The COVID-19 pandemic has changed dramatically the way how universities ensure the continuous and sustainable way of educating students. This paper presents the evaluation of the hybrid pedagogic methods in students’ progression in Learning using neural network (NN) modelling and prediction. The hybrid pedagogic approach is based on the revised Bloom’s taxonomy in combination with the flipped classroom, asynchronous and cognitive learning approach. Educational data of labs and class test scores, as well as students’ total engagement and attendance metrics for the programming module are considered in this study. Conventional statistical evaluations are performed to evaluate students’ progression in learning. The NN is further modelled with six input variables, two layers of hidden neurons, and one output layer. Levenberg-Marquardt algorithm is employed as the back propagation training rule. The performance of neural network model is evaluated through the error performance, regression, and error histogram. Overall, the NN model presents how the hybrid pedagogic method in this case has successfully quantified students’ progression in learning throughout the COVID-19 period

Student motivations for studying online: A qualitative study

The availability of online courses has continued to grow over recent years with more students now turning to online offerings. The flexibility offered through online learning is attractive to prospective students with some of the benefits including reduced costs, and the potential to increase and diversify the student body. Online courses provide the advantage of reaching those who may be ‘too busy’ for traditional study, and offer flexibility through anywhere, anytime access. While these benefits may attract prospective learners to the online environment there remains little empirical evidence for the reasons students actually make the decision to study online over more traditional means. Here, it is important to understand students’ motivations for choosing an online course. Without this information universities cannot assess if their programs are effectively designed to meet students’ expectations, or that students are sufficiently informed and prepared for instruction and learning in the online environment. As part of a PhD, research is currently underway investigating what students expect when commencing an online course at Edith Cowan University (ECU). This paper discusses findings relating to the motivation and reasons why first year students decide to study a course online at ECU

Intelligent Support System for Personalized Online Learning

The current conditions of the COVID-19 pandemic have required universities to transfer educational processes in the online environment. eLearning systems provide educational institutions and students with the opportunity to effectively organize the educational process and share knowledge. They provide each student with freedom of access to information and flexibility of the learning process. The student can individually determine the duration and sequence of courses by changing the trajectory of the educational process following their needs. In the context of the pandemic, students and teachers have to optimize their work over the Internet. This requires more extended personalization of the learning process. Intelligent technologies allow you to construct personalized learning paths for each student, varying methods, forms, and speed of learning. This study presents the architecture of the e-learning support system for the selection of online resources and for including them in the student's learning path. The system developed as a set of personal agents and services that interact based on a set of interconnected ontological models. Ontologies provide a more adequate representation of online resources and compatibility of the user request format with descriptions of training resources from different developers. The system recommends training modules based on current requests and user characteristics that match their profile. The system dynamically updates the knowledge base user characteristics, thereby increasing the effectiveness of recommendations.</em

ANALYSIS OF THE QUALITY OF WORKING LIFE OF A HIGHER EDUCATION INSTITUTION IN THE SOUTH OF SONORA, THROUGH THE ANALYSIS OF ARTIFICIAL NEURAL NETWORKS

The purpose of this study is to investigate and analyze the Quality of work life of a Higher Education institution in the South of Sonora, through the analysis of artificial neural networks; where a result of 4.75 was obtained on a scale of one to five; considering it to percentage a general 95% of the study. Where the population under study 58% is married, 51% academic staff, and 49 administrative. With a level of studies 61% postgraduate, 28% undergraduate and 11% preparatory. Regarding the result of the variables, the best evaluated is quality of life with 100%, quality of life at work with 95%, organizational performance with 94% and organizational management with 93%. Regarding the summary of the model, it is observed that there is a significant relationship to the female sex with 55.7% and in terms of the predominant age it ranges between 31 to 40 years with 42.9%. It was possible to observe the correlation of results with the methods used in this research of the focus group and the use of artificial neural networks, obtaining that the most influential dependent variable of the quality of life at work is the type of hiring of contract personnel and being the reagent I am proud of my work that I do in the institution with the highest degree of importance. The model shows the importance of the 100% normalized independent variables of being proud of the work you do in the institution

Student Perceptions Analysis of Online Learning: A Machine Learning Approach

The covid-19 pandemic is currently occurring affects almost all aspects of life, including education. School From Home (SFH) is one of the ways to prevent the spread of Covid-19. The face-to-face learning method in class turns into online learning using information technology facilities. Even though there are many barriers to implementing classes online, online learning provides a new perspective for students' learning process. One of the factors for the online learning process's success is the interaction between the two main actors in the learning process, i.e., lecturers and students. The study's purpose was to analyze students' perceptions of the online learning process. The research data were obtained from a student questionnaire, which included five main criteria in the learning process: 1) self-management aspects, 2) personal efforts, 3) technology utilization, 4) perceptions of self-roles, and 5) perceptions of the role of the lecturer. Students provide an assessment through a questionnaire about the online learning methods they experience during the Covid-19 pandemic. The random forest algorithm was applied to examine data. The study results were focused on three main criteria (variable importance) that affect students' perceptions of the online learning process. The results described that the students' satisfaction in online learning is influenced by 1) The relationship between students and lecturers. 2) The learning materials need to be changed and adapted to the online learning method; 3) The use of technology to access online learning. The study contributes to improving the online learning method for the student

Key Influencing Factors Affecting the Student Academic Performance and Student Satisfactions Ratings: Evidence from Undergraduate Students in China

This paper has developed a sound and practical method to evaluate the key teaching quality including the student academic performance and student satisfaction ratings. The method makes use of the existing data already readily available in a Chinese university, focusing on the identification of key influencing factors affecting the student academic performance and student satisfactions ratings. The data analyses have shown the university student academic performance is significantly affected student gender, age, previous academic performance, settlements and occupations of parents. There is significant difference in the student ratings for different genders and academic positions of teaching staff. The student performance and satisfaction ratings also significantly vary in different years of intakes and different Schools/programs. The student’s university academic performance can be accurately predicted using artificial neural networks with a prediction error of about 7%. This approach can help the university to improve the student academic performance and student satisfactions

SISTEM KLASIFIKASI KUALITAS KAYU JATI BERDASARKAN JENIS TEKSTUR DENGAN JARINGAN SYARAF TIRUAN MENGGUNAKAN GRAY-LEVEL-CO-OCCURENCE MATRIX

Kayu jati memiliki karakteristik akan kekuatan yang dimilikinya dalam ketahanan berbagai perubahan cuaca. Kayu jenis ini banyak dimanfaatkan dalam keperluan rumah tangga atau kebutuhan perindustrian lainnya. Selain itu memiliki karakter tekstur dan serat yang menjadi ciri khas tersendiri. Secara kasat mata mungkin akan sulit membedakan jenis kualitas kayu jati ini. Citra kayu dapat dibedakan dengan jenis tekstur serat. Identifikasi ini dapat dilakukan dengan proses analisis citra kayu, pemrosesan citra, identifikasi ciri, dan kemudian pengklasifikasian. Identifikasi ciri ini menggunakan Gray-level-co-occurrence(GLCM) kemudian melakukan klasifikasi degan jaringan syaraf tiruan. Metode ini digunakan untuk membantu sistem melakukan pengenalan pola tekstur serat melalui nilai kontras, korelasi, homogenitas dan energy. Keluaran yang dihasilkan memiliki akurasi sebesar 98,3%. Dari 60 data yang diujikan pada sistem hanya terdapat 2 kesalahan antara data asli dan data hasil pengujian

Modelling, prediction and classification of student academic performance using artificial neural networks

The conventional statistical evaluations are limited in providing good predictions of the university educational quality. This paper presents an approach with both conventional statistical analysis and neural network modelling/prediction of students’ performance. Conventional statistical evaluations are used to identify the factors that likely affect the students’ performance. The neural network is modelled with 11 input variables, two layers of hidden neurons, and one output layer. Levenberg–Marquardt algorithm is employed as the backpropagation training rule. The performance of neural network model is evaluated through the error performance, regression, error histogram, confusion matrix and area under the receiver operating characteristics curve. Overall, the neural network model has achieved a good prediction accuracy of 84.8%, along with limitation

Supervised Learning Algorithms in Educational Data Mining: A Systematic Review

The academic institutions always looking for tools that improve their performance and enhance individuals outcomes. Due to the huge ability of data mining to explore hidden patterns and trends in the data, many researchers paid attention to Educational Data Mining (EDM) in the last decade. This field explores different types of data using different algorithms to extract knowledge that supports decision-making and academic sector development. The researchers in the field of EDM have proposed and adopted different algorithms in various directions. In this review, we have explored the published papers between 2010-2020 in the libraries (IEEE, ACM, Science Direct, and Springer) in the field of EDM are to answer review questions. We aimed to find the most used algorithm by researchers in the field of supervised machine learning in the period of 2010-2020. Additionally, we explored the most direction in the EDM and the interest of the researchers. During our research and analysis, many limitations have been examined and in addition to answering the review questions, some future works have been presented

Empowering educators to be AI-ready

In this paper, we present the concept of AI Readiness, along with a framework for developing AI Readiness training. ‘AI Readiness’ can be framed as a contextualised way of helping people to understand AI, in particular, data-driven AI. The nature of AI Readiness training is not the same as merely learning about AI. Rather, AI Readiness recognises the diversity of the professions, workplaces and sectors for whom AI has a potential impact. For example, AI Readiness for lawyers may be based on the same principles as AI Readiness for Educators. However, the details will be contextualised differently. AI Readiness recognises that such contextualisation is not an option: it is essential due to the multiple intricacies, sensitivities and variations between different sectors and their settings, which all impact the application of AI. To embrace such contextualisation, AI Readiness needs to be an active, participatory training process and aims to empower people to be more able to leverage AI to meet their needs. The text that follows focuses on AI Readiness within the Education and Training sector and starts with a discussion of the current state of AI within education and training, and the need for AI Readiness. We then problematize the concept of AI Readiness, why AI Readiness is needed, and what it means. We expand upon the nature of AI Readiness through a discussion of the difference between human and Artificial Intelligence, before presenting a 7-step framework for helping people to become AI Ready. Finally, we use an example of AI Readiness in action within Higher Education to exemplify AI Readiness