Predicting College placements based on academic performance is critical to supporting educational institutions and students in making informed decisions about future career paths. The present research investigates the use of Machine Learning (ML) algorithms to predict college students' placements using academic performance data. The study makes use of a dataset that includes a variety of academic markers, such as grades, test scores, and extracurricular activities, obtained from a varied sample of college students. To create predictive models, the study analyses numerous ML algorithms, including Logistic Regression, Gaussian Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbour. The predictive models are evaluated using performance criteria such as accuracy, precision, recall, and F1-score. The most effective machine learning method for forecasting students' placements based on academic achievement is identified through a comparative study. The findings show that Random Forest approaches have the potential to effectively forecast college student placements. The findings show that academic factors such as grades and test scores have a considerable impact on prediction accuracy. The findings of this study could be beneficial to educational institutions, students, and career counsellors.

Large Language Models (LLMs) have received significant attention due to their potential to transform the field of education and assessment through the provision of automated responses to a diverse range of inquiries. The objective of this research is to examine the efficacy of three LLMs - ChatGPT, BingChat, and Bard - in relation to their performance on the Vietnamese High School Biology Examination dataset. This dataset consists of a wide range of biology questions that vary in difficulty and context. By conducting a thorough analysis, we are able to reveal the merits and drawbacks of each LLM, thereby providing valuable insights for their successful incorporation into educational platforms. This study examines the proficiency of LLMs in various levels of questioning, namely Knowledge, Comprehension, Application, and High Application. The findings of the study reveal complex and subtle patterns in performance. The versatility of ChatGPT is evident as it showcases potential across multiple levels. Nevertheless, it encounters difficulties in maintaining consistency and effectively addressing complex application queries. BingChat and Bard demonstrate strong performance in tasks related to factual recall, comprehension, and interpretation, indicating their effectiveness in facilitating fundamental learning. Additional investigation encompasses educational environments. The analysis indicates that the utilization of BingChat and Bard has the potential to augment factual and comprehension learning experiences. However, it is crucial to acknowledge the indispensable significance of human expertise in tackling complex application inquiries. The research conducted emphasizes the importance of adopting a well-rounded approach to the integration of LLMs, taking into account their capabilities while also recognizing their limitations. The refinement of LLM capabilities and the resolution of challenges in addressing advanced application scenarios can be achieved through collaboration among educators, developers, and AI researchers.

With the rapid and constant changes in computer and information technology, the content and learning methods in Computer Science related courses need to be continuously adapted and consistently aligned with the latest developments in the field. This paper proposes a learning approach called the Gallery-walk integrated Project-Based Learning (G-PBL) which can develop students’ lifelong learning skills that are extremely crucial for Computer Science students. The G-PBL was designed by incorporating the advantages of Project-Based Learning (PBL) and gallery walk learning strategy. In contrast to traditional PBL where students may present their project work to instructors only, students have to present their project work to their classmates as part of the G-PBL approach. All students are required to evaluate their peers’ project work and then give feedback and suggestions. For the research experiments, the G-PBL was implemented as an instructional approach in two Computer Science related courses. This study focuses on exploring the differences in knowledge gain, learning motivation, and perceived usefulness when learning by using the teacher-centered and G-PBL approach. Moreover, the impact of gender differences on learning outcomes is also investigated. The results reveal that using the G-PBL approach helps students to gain more knowledge significantly, for both male and female students. In terms of motivation, female students are more favorable toward the G-PBL approach. On the contrary, male students prefer learning via a teacher-centered approach. Regarding the perceived usefulness, female students strongly view the G-PBL as a highly effective learning approach, whereas male students are more prone to concur that the teacher-centered approach is a more effective learning method.

Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field.

Due to the COVID-19 situation, all activities, including education, were shifted to online platforms. Consequently, instructors encountered increased challenges in evaluating students. In traditional assessment methods, instructors often face ambiguous cases when evaluating students’ competencies. Recent research has focused on the effectiveness of fuzzy logic in assessing students’ competencies, considering the presence of uncertain factors or multiple variables. Additionally, demographic characteristics, which can potentially influence students’ performance, are not typically utilized as inputs in the fuzzy logic method. Therefore, analyzing students’ performance by incorporating these factors is crucial in suggesting adjustments to teaching and learning strategies. In this study, we employ a combination of fuzzy logic and hierarchical linear regression to analyze students’ performance. The experiment involved 318 students from various programs and showed that the hybrid approach assessed students’ performance with greater nuance and adaptability when compared to a traditional method. Moreover, the findings in this study revealed the following: 1) There are differences in students’ performance between traditional and fuzzy evaluation methods; 2) The learning method is an impact on students’ fuzzy grades; 3) Students studying online do not perform better than those studying onsite. These findings suggest that instructors and educators should explore effective strategies being fair and suitable in assessment and learning.

Technology has changed the way we teach and the way we learn. Many learning theories can be used to apply and integrate this technology more effectively. There is a close relationship between technology and constructivism, the implementation of each one benefiting the other. Constructivism states that learning takes place in contexts, while technology refers to the designs and environments that engage learners. Recent efforts to integrate technology in the classroom have been within the context of a constructivist framework. The purpose of this paper is to examine the definition of constructivism, incorporating technology into the classroom, successful technology integration into the classroom, factors contributing to teachers’ use of technology, role of technology in a constructivist classroom, teacher’s use of learning theories to enable more effective use of technology, learning with technology: constructivist perspective, and constructivism as a framework for educational technology. This paper explains whether technology by itself can make the education process more effective or if technology needs an appropriate instructional theory to indicate its positive effect on the learner.

It is important to study learning styles because recent studies have shown that a match between teaching and learning styles helps to motivate students´ process of learning. That is why teachers should identify their own teaching styles as well as their learning styles to obtain better results in the classroom. The aim is to have a balanced teaching style and to adapt activities to meet students´ style and to involve teachers in this type of research to assure the results found in this research study. Over 100 students complete a questionnaire to determine if their learning styles are auditory, visual, or kinesthetic. Discovering these learning styles will allow the students to determine their own personal strengths and weaknesses and learn from them. Teachers can incorporate learning styles into their classroom by identifying the learning styles of each of their students, matching teaching styles to learning styles for difficult tasks, strengthening weaker learning styles. The purpose of this study is to explain learning styles, teaching styles match or mismatch between learning and teaching styles, visual, auditory, and kinesthetic learning styles among Iranian learners, and pedagogical implications for the EFL/ESL classroom. A review of the literature along with analysis of the data will determine how learning styles match the teaching styles.

Entrepreneurship is the key driver of economic progress in many countries; thus, many countries have introduced policies to promote a more entrepreneurial environment. This study assesses the impact of factors affecting entrepreneurial intention of university students. The data was collected through a survey of 341 students at 09 leading universities in Hanoi, Vietnam and analyzed using structural equation modeling (SEM) with SPSS and Amos software. The research results show that entrepreneurial skills, entrepreneurial environment and subjective norms either directly or indirectly affect business motivation and entrepreneurial intention of university students. Thus, it is suggested that university and other educational institutions should provide more activities and taught courses that help students acquire the knowledge and skills necessary for entrepreneurship.

The use of multimedia in teaching and learning leads to higher learning. Multimedia refers to any computer-mediated software or interactive application that integrates text, color, graphical images, animation, audio sound, and full motion video in a single application. Multimedia learning systems offer a potentially venue for improving student understanding about language. Teachers try to find the most effective way to create a better foreign language teaching and learning environment through multimedia technologies. In this paper, the researcher defines multimedia, elaborates the rationale for using multimedia, identifies multimedia learning, mentions principles of multimedia, explains theoretical basis of multimedia English teaching, reviews roles of teachers and learners in multimedia environment, discusses the relationship between multimedia and learning, and states the strength of multimedia English teaching. The review of literature shows that teachers need to make full use of multimedia to create an authentic language teaching and learning environment where students can easily acquire a language naturally and effectively.

A group of researchers and developers from Colombia and Mexico have recognised that the development of state-of-the-art Extended Reality software, a key technology for the Metaverse, has great potential to improve teaching-learning processes in educational institutions. However, the development process does not take into account accessibility, universal design and inclusion, especially for the deaf student community. An extended reality model is proposed for the creation of this type of software as a tool to support access to knowledge, based on information gathering, requirements analysis, user-centred design and video game programming, including the ludic and didactic. The aim is to minimise the barriers that limit the learning of programming logic by students with hearing disabilities through the use of new technologies, creating spaces in virtual worlds that are understandable, usable and practical in conditions of safety, comfort and as much autonomy as possible. To validate the model, a mixed reality software prototype was designed and programmed to train students in programming logic, both deaf and hearing. User and heuristic tests were carried out, showing how immersion can improve knowledge acquisition processes and develop skills in higher education students.

Predicting College placements based on academic performance is critical to supporting educational institutions and students in making informed decisions about future career paths. The present research investigates the use of Machine Learning (ML) algorithms to predict college students' placements using academic performance data. The study makes use of a dataset that includes a variety of academic markers, such as grades, test scores, and extracurricular activities, obtained from a varied sample of college students. To create predictive models, the study analyses numerous ML algorithms, including Logistic Regression, Gaussian Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbour. The predictive models are evaluated using performance criteria such as accuracy, precision, recall, and F1-score. The most effective machine learning method for forecasting students' placements based on academic achievement is identified through a comparative study. The findings show that Random Forest approaches have the potential to effectively forecast college student placements. The findings show that academic factors such as grades and test scores have a considerable impact on prediction accuracy. The findings of this study could be beneficial to educational institutions, students, and career counsellors.

There appears to be a tendency for the strategies and methods that have been offered in OOP course learning to affect the improvement of individual skills only. There is a significant need for learning strategies which are relevant and able of improving collaborative working skills. The purpose of this study is to develop a Collaborative Learning and Programming model suitable for Object-Oriented Programming courses and assess its validity, practicality, and effectiveness. The implementation of the CLP model was conducted using the ADDIE development procedure by involving 7 experts, 35 experimental class students, 23 control class students and 4 lecturers of the Object-Oriented Programming course. The survey results showed that the CLP model was valid, practical, and effective in achieving these goals. The validity test results were verified based on experts' assessment, indicating that the aspects contained in the CLP model were valid with an Aiken's value V =0.89. The practicality test results indicated that the model was highly practical with the practicality value of 89.95% from students and 89.67% from lecturers. Finally, using the CLP model demonstrated its effectiveness in reducing the abstraction and complexity of OOP courses and improving student collaboration, particularly in programming tasks. The significance of conducting this survey is that it provides evidence for the effectiveness of the CLP model in achieving its intended goals and can inform the development of future OOP courses and programming tasks. The survey was conducted well, as it used both expert assessment and student and lecturer feedback to assess the validity, practicality, and effectiveness of the CLP model.

With the rapid and constant changes in computer and information technology, the content and learning methods in Computer Science related courses need to be continuously adapted and consistently aligned with the latest developments in the field. This paper proposes a learning approach called the Gallery-walk integrated Project-Based Learning (G-PBL) which can develop students’ lifelong learning skills that are extremely crucial for Computer Science students. The G-PBL was designed by incorporating the advantages of Project-Based Learning (PBL) and gallery walk learning strategy. In contrast to traditional PBL where students may present their project work to instructors only, students have to present their project work to their classmates as part of the G-PBL approach. All students are required to evaluate their peers’ project work and then give feedback and suggestions. For the research experiments, the G-PBL was implemented as an instructional approach in two Computer Science related courses. This study focuses on exploring the differences in knowledge gain, learning motivation, and perceived usefulness when learning by using the teacher-centered and G-PBL approach. Moreover, the impact of gender differences on learning outcomes is also investigated. The results reveal that using the G-PBL approach helps students to gain more knowledge significantly, for both male and female students. In terms of motivation, female students are more favorable toward the G-PBL approach. On the contrary, male students prefer learning via a teacher-centered approach. Regarding the perceived usefulness, female students strongly view the G-PBL as a highly effective learning approach, whereas male students are more prone to concur that the teacher-centered approach is a more effective learning method.

Due to the COVID-19 situation, all activities, including education, were shifted to online platforms. Consequently, instructors encountered increased challenges in evaluating students. In traditional assessment methods, instructors often face ambiguous cases when evaluating students’ competencies. Recent research has focused on the effectiveness of fuzzy logic in assessing students’ competencies, considering the presence of uncertain factors or multiple variables. Additionally, demographic characteristics, which can potentially influence students’ performance, are not typically utilized as inputs in the fuzzy logic method. Therefore, analyzing students’ performance by incorporating these factors is crucial in suggesting adjustments to teaching and learning strategies. In this study, we employ a combination of fuzzy logic and hierarchical linear regression to analyze students’ performance. The experiment involved 318 students from various programs and showed that the hybrid approach assessed students’ performance with greater nuance and adaptability when compared to a traditional method. Moreover, the findings in this study revealed the following: 1) There are differences in students’ performance between traditional and fuzzy evaluation methods; 2) The learning method is an impact on students’ fuzzy grades; 3) Students studying online do not perform better than those studying onsite. These findings suggest that instructors and educators should explore effective strategies being fair and suitable in assessment and learning.

Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field.

The foundational tenet of any nation's prosperity, character, and progress is education. Thus, a lot of emphasis is laid on quality of education and education delivery system in India with current financial year (2022-23) education budget outlay of Rs. 1,04,277.72 crores. This research contributes in analyzing how students perform in academics depending upon the time spent on their extracurricular activities with the help of three Machine Learning prediction algorithms namely Decision Tree, Random Forest and KNN. Additionally, in order to comprehend the underlying causes of the shortcomings in each machine learning technique, comparisons of the prediction outcomes obtained by these various techniques are made. On our dataset, the Decision Tree outscored all other algorithms, achieving F1 84 and an accuracy of 85%. The research, which is at an introductory level, is meant to open the door for more complexes, specialised, and in-depth studies in the area of predicting the performance in academics.

The development of methods for assessing lecturers' performance is needed to motivate lecturers to achieve institutional targets. Currently, lecturers are required to be able to adapt to the rapid development of technology. Lecturer performance assessment must be done periodically. Competence is measured as a basis for planning resource development activities. The method that is often used for assessing lecturer performance is the Simple Additive Weighting (SAW) method. However, the SAW method has drawbacks, namely 1) the process of determining criteria is only carried out by the leadership (subjective); 2) The SAW method can only be applied to multi-criteria data ; 3) Data ranking problems. Based on this deficiency, a new method was built, namely, the Weighted Performance Indicator (WPI) method using respondents’ opinion to determine the criteria. This study aims to compare the performance of the two methods. Testing criteria using SPPS application dan WPI method, while testing methods utilized the SAW method and the WPI method. The results of the criterion test show the Percentage of Similarity of data validity = 96.7 % witht the minimum percentage limit (MPL) = 40%. While the results of the SAW method and WPI method testing resulted in the highest score in the 13th alternative, namely SAW score (v13) = 793.76 and WP score (WP13) = 0.928, and the lowest value in the 30th alternative, SAW score (v30) = 18.60 and WP score (WP30) = 0.140. the ranking positions in these two methods show similarities. However, for other alternatives, the rating value can be different. 
The WPI method is a scientific development in the field of decision support systems that can be applied to other performance assessments, such other human resources, system performance assesment etc. 
The results of this study prove that the WPI method can be used as a performance assessment method with different characteristics from the SAW method.

Entrepreneurship is the key driver of economic progress in many countries; thus, many countries have introduced policies to promote a more entrepreneurial environment. This study assesses the impact of factors affecting entrepreneurial intention of university students. The data was collected through a survey of 341 students at 09 leading universities in Hanoi, Vietnam and analyzed using structural equation modeling (SEM) with SPSS and Amos software. The research results show that entrepreneurial skills, entrepreneurial environment and subjective norms either directly or indirectly affect business motivation and entrepreneurial intention of university students. Thus, it is suggested that university and other educational institutions should provide more activities and taught courses that help students acquire the knowledge and skills necessary for entrepreneurship.

One crucial and intricate problem in the education sector that must be dealt with is children who initially enrolled in schools but later dropped out before finishing mandatory primary education. These children are generally referred to as out-of-school children. To contribute to the discuss, this paper presents the development of a robust Multilayer Perceptron (MLP) based Neural Network Model (NN) for optimal prognostic learning of out-of-school children trends in Africa. First, the Bayesian optimization algorithm has been engaged to determine the best MLP hyperparameters and their specific training values. Secondly, MLP-tuned hyperparameters were employed for optimal prognostic learning of different out-of-school children data trends in Africa. Thirdly, to assess the proposed MLP-NN model's prognostic performance, two error metrics were utilized, which are the Correlation coefficient (R) and Normalized root means square error (NRMSE). Among other things, a higher R and lower NRMSE values indicate a better MLP-NN precision performance. The all-inclusive results of the developed MLP-NN model indicate a satisfactory prediction capacity, attaining low NRMSE values between 0.017 - 0.310 during training and 0.034 - 0.233 during testing, respectively. In terms of correlation fits, the out-of-school children's data and the ones obtained with the developed MLP-NN model recorded high correlation precision training/testing performance values of 0.9968/0.9974, 0.9801/0.9373, 0.9977/0.9948 and 0.9957/0.9970, respectively. Thus, the MLP-NN model has made it possible to reliably predict the different patterns and trends rate of out-of-school children in Africa. One of the implications for counselling, among others, is that if every African government is seriously committed to funding education at the foundation level, there would be a reduction in the number of out-of-school children as observed in the out-of-school children data.

During COVID-19 pandemic, most tertiary institutions in Ghana were compelled to continue delivering of lectures online using internet technologies as was in the case of other countries. Senior high schools in Ghana were, however, not asked to do same, currently, the setting of most literature on blended or online learning in Ghana is focused on tertiary education. This paper situates the blended learning model in a less endowed senior high school to unearth the prospect of its implementation. The research provides an alternative to the traditional face-to-face learning, which is faced with the challenge of inadequate infrastructure, high number of students to class ratio, less compatibility with 21st learning skills and long-life learning in Ghana.
A customed Moodle application as web application tool, hosted students online in both synchronous and asynchronous interactions. Purposive quota sampling size technique was used to select an appreciable sample size to fully go through the traditional face-face model for a term and then study through the blended learning model for another term. Students’ examination performances for both were analyzed with a paired t test statistical model. Interviews with participants were conducted to ascertain their evaluation of the blended learning model and questionnaires were also administered to discover the institutional, technological, and human resource readiness for blended learning in senior high schools. The analysis of the data gathered, proved that blended learning in senior high schools has high prospect and is better alternative to face-to-face learning in Ghana.