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This paper conducted a questionnaire survey to investigate the cooperative and competitive personality of 171 primary school students in the robotics course. Results indicated: (1) The cooperative personality of students is significantly higher than that of competition. (2) There is no significant difference in cooperative and competitive personality between boys and girls. (3) The correlation coefficient between students' cooperative personality and their academic achievement in the robotics course failed to reach significance. (4) There is also a very low-level correlation between students' competitive personality and their academic achievement in the robotics course. Accordingly, this paper proposes that teachers can apply the co-opetition design in the robotics course to provide multiple paths for students with different cooperative and competitive personalities.

This Full Research paper presents a study to determine the correlations between student involvement in an academic makerspace and academic achievement as measured by GPA. University makerspaces are open environments designed to encourage creative collaborations and innovative exploration by providing students access to a variety of machines and tools typically focused on rapid prototyping. In aneffort to understand the benefits these spaces in academia, there have been several studies on their founding and facilitation; however, there is a lack of data-driven studies of student involvement and the impact of makerspace use on student development.This paper presents preliminary results from an investigation to so show the impact of involvement in an academic makerspace on GPA. By measuring cohorts of students at different stages in the mechanical engineering curriculum, correlations are made between the level ofinvolvement in makerspaces and GPA. The results indicate that students who use the space to build and create, either by choice or for a course requirement, have been shown to have a higher average in-major GPA thanstudents who do not use the space. These findings and more encourage the further exploration of the impact of makerspaces through both quantitative and qualitative methods.

Student-centered learning requires students taking responsibility for their own learning, and becoming autonomous learners. Using a mixed methodology approach with a sequential explanatory design, this paper reports some results from an ongoing research about learner autonomy of mechanical engineering students (first cycle) in Portugal. For the purpose of this paper, the focus is the relationship between learner autonomy and academic achievement and the way it translates to students’ perceptions of autonomy in learning, its characteristics and importance, and how having students talk about learner autonomy can be useful to improve their learning and build a bridge between research and practice in Engineering Education. The results show that students have positive perceptions about their own learner autonomy and its importance. A positive moderated statistically significant correlation was found between learner autonomy and academic achievement, which is mainly due to the control dimension of learner autonomy. Students lack initiative, with this dimension being the one that least contributes to learner autonomy, and having no statistically significant correlation with academic achievement. Because of this, promoting actions that increase students taking initiative seems to be a way of improving learner autonomy. However, the actions taken have to consider that for some students, learner autonomy requires perseverance and is difficult to maintain. So, even though for most students, having opportunities to improve may be enough, less autonomous students may require that the action of teachers and the curricular activities proposed, promotes an academic environment that encourages and supports autonomy in learning.

Soft and hard skills have become a challenging issue for Information Systems (IS) graduates and recruiters in Saudi industry. IS students are lacking the skills that are required by Saudi industry. Recruiters, on the other hand, consider the GPA as a major factor for hiring IS candidates. This paper discusses the impacts of self-regulated learning strategies and academic achievements on matching the required skills of Saudi industry. Therefore, it identifies the most required skills of IS jobs in Saudi industry and how the skills of IS students in major Saudi universities can match them. Two questionnaires were distributed, one for recruiters and another for students. First questionnaire is to assess the required IS skills in Saudi industry by recruiters. Second questionnaire is to capture the skills, self-regulated learning (SRL), and academic achievement of IS students. The collected data was used to develop a classification model using Decision Tree, Naive Bayes, and Nearest Neighbor algorithms to predict the suitability of IS graduates to the Saudi industry. The results show that the Naive Bayes algorithm performed the best (with accuracy 69% and ROC 0.62). Finally, this paper demonstrated a novel way to predict student skills’ suitability for the industry and thereby helping the universities to design better curriculum and the students to prepare better for the job market.

Today's rapidly changing in Technology Enhanced Learning (TEL) and Lifelong Learning Styles (LLS) are becoming a part of everyday life. This objectives of the paper is to develop a theoretical framework and provide perspectives of Learning Strategies (LS) and Learning Achievement (LA) for Lifelong learning at the high school in the Maha Sarakham province. The paper is focused on identifying the first steps needed to make learning academic achievement. The data gathering is collected from 668 students at Phadungnaree School, Maha Sarakham province. The method consists of three processes: data collection, data analysis and model development, and evaluation of model performance. The results of the study found that the factors that affect students are basic chemistry course (s30102), basic biology course (s30103), and physics course (s30101). Finally, the testing results and measurement the performance of the model are high scores (94.51%), which can be further developed into software applications and mobile applications.

The objective of this paper was to discover the hidden academic relationship between academic achievement and higher education institutions. This paper presented an approach that could be utilised to identify the sets of attributes by combining decision tree and attribute selection. The main idea involves using techniques from the field of data mining to select important attributes, which can then be used to classify a hidden academic relationship. The dataset was collected from 288 students at 7 schools located in Maha Sarakham and Kalasin Provinces, Thailand. The best model showed an accuracy of 82.99%, meaning the significant attributes could be used to conduct further research.

This paper highlights the importance of using student data to drive improvement in education planning. It then presents techniques of how to obtain knowledge from databases such as large arrays of student data from academic Institution databases. Further, it describes the development of a tool that will enable faculty members to identify, predict and classify students based on academic performance measured using Cumulative Grade point average (CGPA) grades. The need for prediction of a student's performance is to enable the university to intervene and provide assistance to low achievers as early as possible. Included in the paper is a brief overview of the most commonly used classifiers techniques in educational data mining and an outline of the use of Neuro-Fuzzy classification in a case study research to predict and classify students' academic achievement in an Electrical Engineering faculty of a Malaysian public university.

The reality interactive technology contributes to add some real interactive teaching concepts and phenomena in science, and the creation of an interactive learning environment for active SAFE mimic reality, so interactive Simulations in teaching science is a fundamental pillar for the development of understanding of the learners to the concepts of scientific facts more attractive and suspense of the learner methods, known researcher simulation interactive in this paper as "learning environments and teaching used to give scientific concepts to students through testing materials science laboratories electronic mimic real laboratory interactive, and provides the same circumstances, but it is less expensive and more safety and accuracy in achieving results." The Natural Sciences of the most important areas that the latest computer and its applications a great revolution in education, so they are considered natural science materials from more subjects related to technology, whether cognitive or through the integration of this technology in the scientific integrated student who growth strives to be a meaningful through interaction instruction direct with interactive lab. And deals with the researcher in this paper is a theoretical introduction about the technological revolution and its relation to the educational process, and theoretical introduction about the importance of interactive simulation in teaching science, and a review of several definitions for interactive simulation by the views of the researchers. And also it addresses the researcher in this paper are the main components of the interactive simulation types, and features interactive science education in the simulation, and the challenges that limit the use of interactive simulation in teaching science. The presentation of some Arab and foreign studies, which focused on measuring the effectiveness of interactive treatment on academic achievement and science to students through their use in the teaching of science, and display models of international experiences in the use of interactive simulations in science. The recommendations, including the need for a well thought out practical plans to activate the use of interactive simulation in teaching science, and the need to provide funding and design appropriate programs and training programs for those in charge of interactive simulation.

The current paper aims to identify the effect of using the Cyber Hunt Strategy on the development of academic achievement and the retention of the impact of learning in mathematics among undergraduate students in Jordan. To achieve the aims of the study, a Cyber Hunt Strategy in a form of an educational website is designed and a test to measure academic achievement. The results show the effectiveness of this strategy in developing academic achievement in mathematics of students.

The study's aim is to see how academic achievement and student Intelligence Quotient influence placement. This paper will attempt to predict whether a student's intelligence quotient or academic score plays a significant role in placement. On a dataset of 193 students, we used a machine learning algorithm to compare the impact of student intelligence, behavior, and academic achievement on placement. We have used a Voting Classifier architecture to predict and classify the probability of a student being placed or not. The motivating force behind this research was to figure out why a group of students scoring the same marks in the same branch studying under the supervision of the same faculty are not able to fulfill the demands of an organization in order to be employed. The aim of this research was to combine conceptually different machine learning classifiers and predict the probability of a student being hired using a majority vote or the average expected probabilities. A classifier like this can be useful for balancing out the weaknesses of a group of models that are all performing well. Experiments show that student intelligence and attitude play a significant role in the recruiting process.

[EN] Neuropsychological assessment has been traditionally made through paper-and-pencil batteries which usually are time-consuming, decontextualized, and nonecological. These abilities play a critical role in education since they are very related to learning capacity, academic achievement, social functioning, as well as the inhibition of maladaptive behaviors. Meanwhile, serious games are being used in education and psychology to achieve assessments without these limitations, including neuropsychological assessments. While traditional tests can be analyzed with classical statistics, a large number of variables can be extracted from serious games, the analysis of which can be more complex. Machine learning can handle this large amount of information and find patterns that allow us to recognize behaviors. This study aimed to investigate whether machine learning could be used to improve predictive validity in applying a serious game for neuropsychological assessment. Results were based on 60 subjects, including 42 cognitive activities. The validation process showed best results on attention, memory, planning, and cognitive flexibility, achieving accuracies higher or equal to 0.8 and Cohen¿s Kappas higher than 0.55, which implies that the Virtual Serious Game could be a valid tool to perform a neuropsychological evaluation along with traditional tests., This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness funded project Advanced Therapeutically Tools for Mental Health (DPI2016-77396-R) and by the European Union through the Operational Program of the European Regional Development Fund (ERDF) on the Valencian Community 2010-2020 (IDIFEDER/2018/029).

The social, emotional, economic and cultural characteristics of students have emerged in literature showing their importance in student learning and academic achievement. Proactivity has been recently seen to be an essential attribute for success in academic as well as non-academic areas. The research paper considers the possible association between proactivity and ethnicity. Three measures of proactivity namely proactive personality, proactive confidence and proactive behaviour, were used. The Kruskal-Wallis test showed that proactivity levels differed significantly between the ethnic groups - Melanesian, Micronesian, Polynesian and Fiji Indians. The difference was attributed to the Pacific way of life and the unique culture and tradition of the different ethnic groups of the South Pacific.

This work-in-progress research paper contains the findings from a meta-analysis on the effects of using the engineering design process on K–12 students’ science and mathematics achievement. This study uses a meta-analysis methodology to calculate the effect size of the integration of the engineering design process into science and mathematics teaching and learning practices on student’s science and math achievements. A sample of empirical studies published during or after 2018 were included, and data were analyzed using STATA 16 statistical software. Analysis revealed a statistically significant effect size (d = 0.31 [CI= 0.18,0.44]) on students’ science and mathematics achievement. The findings suggest that implementing the engineering design process enhances students’ learning experiences and improves their academic achievement in science and mathematics. The results of this WIP meta-analysis should inform instructional leaders and other education stakeholders concerning the importance of integrating the engineering design process into Science, Technology, Engineering, and Mathematics teaching and learning practices.

The prediction of students' academic achievement is crucial to be conducted in a university for early detection of students at risk. This paper aims to present data mining models using classification methods based on Decision Trees (DT) algorithms to predict students' academic achievement after preparatory year, and to identify the algorithm that yields best performance. The students' academic achievement is defined as High, Average, or Below Average based on graduation CGPA. Three classifiers (J48, Random Tree and REPTree) are applied on a newly created dataset consisting of 339 students and 15 features, at the College of Computer Science and Information Technology (CCSIT). The outcome showed the J48 algorithm had an overall superior performance compared to other algorithms. Feature selection algorithms were used to reduce the feature vectors from 15 to 4 resulting in improvements in performance and computational efficiency. Finally, the results obtained help to pinpoint the preparatory year courses that impact graduation CGPA. Timely warnings, and preemptive counseling towards improving academic achievement is possible now.

This research paper presents AWC 708C laser cutting manual learning set. It is a learning set which is relevant to high school courses and taught in a module (namely 30291 Creativity and Innovation). The researchers have developed a combination between the augmented reality technology and the AWC708C laser cutting learning kit to make them more attractive and reduce danger to users by adding additional security systems to the device. Also, it includes teaching how to use the machine through a designed worksheet to accompany with an augmented reality technology which is intended to be suitable for high school students in grade 10 (Mathayomsuksa 4). The results of this research found that overall quality of the AWC 708C laser cutting learning kit is in good quality level with average of 4.48±0.58. Outstanding results from an overall quality assessment are the convenience in preparing the equipment and the simple method of using the machine. Also, the quality level of the worksheet is in good quality level with average of 4.24±0.66. In addition, it was found that the results of the evaluation on satisfaction with the AWC 708C laser cutting machine learning kit is in very good level with average of 4.77±0.42. From the comparison of academic achievement between before and after using the learning set analyzed by t-test, it is found that the pre and post test scores of the sample groups had an average score of 5.50 and 8.96, respectively. From t distribution table which has df = 23, confidence value at 0.05 level with t = 2.707, the analysis result is 15.37 higher than the t distribution table. It is implied that the results of the pre-test achievement were different from the post-test achievement scores with significance level of 0.05. From the research hypothesis, it is found that those who studied with AWC708C laser cutting manual learning set had academic achievement after studied higher than before, according to the assumptions. Hence, in this research it can be concluded that the quality of the AWC 708C laser cutting machine learning kit can be increased.

With the rapid development of Internet technology and society, China has entered the era of big data. While this technology has made great contributions to the society, it has also brought some disadvantages. Due to excessive information and wrong values, students are easy to make wrong attribution for academic failure, as well as produce anxiety, depression and other negative academic emotions. Therefore, it is necessary to study students' academic emotion, which is considered as an important factor affecting their academic performance and physical and mental health. Previous studies have only explored the effect of academic achievement attribution on academic emotion. The purpose of this paper is to study whether the relationship between academic achievement attribution and academic emotion is mediated by parents' expectations. Study was based on a cross-sectional design ( $\mathrm{N}=800$ ; mean age=13.22) of students from 8 different classes. The study was conducted through questionnaires. The results of the study: (1) The junior high school students' attribution of academic achievement, academic emotion and parental expectations were all correlated. (2) The internal attribution of students' academic achievement and parents' expectation directly predict the positive academic emotion, while the external attribution of students' academic achievement does not. (3) Academic achievement attribution significantly predicted negative academic emotion, while parental expectations had no significant influence on negative academic emotion. (4) Attributions of junior high school students' academic performance can influence positive and negative academic emotions by influencing parents' expectations. The increase in internal attributions of students' academic achievement promoted an increase in positive parental expectations, which further generated positive academic emotions.

Students are one of the substances that need to be considered in relation to the world of education, because students are translators of the dynamics of science, and carry out the task of exploring that knowledge. As a subject with potential and, at the same time, objects in their activities and creativity, students are expected to be able to develop their qualities. The quality can be seen from the academic achievements achieved, which are evidence of the effort earned by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria to be declared eligible to continue their studies, the student is declared to be not graduating on time or even dropout (DO). The high number of students not graduating on time or dropouts at higher institutions can be minimized by the policies of higher institutions by directing and detecting at-risk students in the early stages of education. Therefore, in this paper, we present the use of Fuzzy Soft Set Classification (FSSC), which is based on the Fuzzy Soft set theory to predict student graduation. The 2068 dataset was taken from the Directorate of Information Systems, Ahmad Dahlan University. The results showed that the FSSC reached up to 0.893292 in terms of accuracy. So, it is expected to be able to detect students at risk in the early stages of education so that higher education can minimize students not graduating on time or dropout by providing appropriate treatment and designing strategic programs.

This paper presents the use of an interactive learning approach in the Engineering Ethics course. Intuitive learning is being implemented in traditional teaching methods, which will engage students in class activities, improve their self-learning skills, and promote active learning. Integrating interactive learning activities has been noted to improve students' learning, course attainment, and learning outcomes. This study is aimed to demonstrate the use of web-based applications (e.g., Quizlet and Kahoot!) to improve students' academic achievement and enhance their engagement in class.

Predicting the student behavior is important as it leads to the scope of improvement in their academic achievement. Association refers to a process of identifying the frequent item sets or patterns from the given dataset in order to provide recommendations about the instances of specific item based on the identified patterns. This paper focuses on association rule mining technique to elicit pragmatic information from the primary data collected. The work carried here explores the relationship between student's intellectual performance and their psychological factors with the use of Apriori algorithm. It aims at predicting and improving academic performance of students by reshaping their psychological parameters.

Educational Data Mining (EDM) is one of the concern areas of data mining used for gathering, analyzing, and presenting information. The purpose of this paper is to analyze online learners' activities to extract hidden information using clustering and classification techniques. The data were collected from learners enrolled in a MOOC course called C programming offered by Kathmandu University of Nepal. For clustering, K-means algorithm was used for grouping of the student with similar characteristic to understand the learners' behavior and for classification, Support Vector Machine (SVM) classifier was implemented to develop predictive model that predicts the students' performance labeled with a class such as low, medium and high. The extracted knowledge can be used by the academic institution to improve teaching and learning processes and improve learner's performance which consequently helps in academic achievement. This research helps in early identification of weak students such that timely decision making can be done to improve learner's performance and reduce online learner's dropout rates.

Presented in this research category full paper is a study on the effectiveness of Texas Science, Technology, Engineering, and Mathematics (T-STEM) high schools on students’ mathematics achievement. The original T-STEM blueprints for funding and designating a school as T-STEM include specifications for raising grade 9 Algebra I End of Course assessment scores. As inclusive STEM high schools, T-STEM schools have been designed with the intention to better serve ethnic minorities, females, and low socio-economic status students by improving both academic achievement and attitude toward STEM fields. The purpose of this study was to examine the effect of attending T-STEM designated schools on students’ mathematics achievement after taking into account prior mathematics achievement, economic disadvantage, gender, and ethnicity. After controlling for these variables, T-STEM schools had no statistically significant effect on Algebra I End of Course assessment scores in the present study. We interpret the results as demonstrating that T-STEM schools are not sufficiently improving students’ mathematics achievement and are thus failing to achieve one of the critical standards set out by the original initiative to create T-STEM schools.

This Full Paper in the Research-category presents our initial findings in regards of students' implicit theories. Beliefs that people associate to themselves, often called implicit theories, self-theories, or mindsets, are a fundamental source of bias in human thinking. Research shows that implicit theories radically shape human thinking, behaviour, and progress. In the domain of education, implicit theories have significant associations to academic performance. In social domains, implicit theories are associated with development of social and soft skills, and groupwork behaviour. This article reviews the most significant research on implicit theories in educational and social domains, and shows significant research gaps in computing education. This article also summarises our previous findings, and presents new results from a sample of $n =39$ second year CS or computer engineering (CE) students in University of Turku, Finland, Department of Future Technologies. We investigated students' implicit theories and related association with academic achievement in first CS courses. Contrary to a lot of research, but in line with our own previous findings, we found that fixed implicit theories were connected with academic achievement. However, more data is needed to add reliability and confirm the results either way. The implications of implicit theories to development of soft skills are discussed, and a future research agenda is laid out.

Peer Assessment plays an important role in facilitating a collaborative learning environment among students in Higher Education. It can be an effective platform for Team-Based activities and to promote cooperative learning. Such approaches are being introduced at different curricula levels as students who engage with cooperative learning tend to exhibit higher academic achievement, better critical thinking and deeper understanding of teaching contents. They can also build more positive and supportive relations with peers which is a crucial organizational skill. However, very few studies have investigated the use of peer assessment for informing cooperative learning in Higher Education. Can Peer Assessment be used to promote cooperative learning among students in Higher Education? This paper explores a second year undergraduate module titled ‘Design, Construction and Test’ in the Department of Electronic Engineering at York. This module has a Peer Review component in one of the assessments. A total of 12 groups participated in this module and using content analysis, their peer review reports were analyzed. The findings highlighted five key factors that contribute towards cooperative learning: delegation, team leadership, attendance, active engagement and effective communication. Groups which implemented these elements within their teams experienced effective collaboration among team members whereas groups which experienced conflicts among peers reflected on the lack of some of these. Peer Assessment gives students an efficient platform to understand the benefits of cooperative learning. To facilitate collaboration in an effective manner, educators must emphasize these factors when exercising Team-Based activities in their curricula.

Studying the effectiveness of remediation courses in higher education has attracted a lot of interest from educational researchers and practitioners. Remediation is associated with significant economic and social costs while the results are usually dubious. In this paper, we applied a widely used method called Regression Discontinuity Design to measure the effectiveness of mathematical remediation courses of the Budapest University ofTechnology and Economics. Our large-scale study is based ondata of 20,000 undergraduate students enrolled between 2010and 2018 spanning 16 semesters. We study both the direct andlonger term effect of remediation and find that it has smallbut statistically significant positive effect on further academicachievement.

This work-in-progress research to practice paper analyzes how previous personal, social and academic variables converge in the explanation of students' academic performance, during the 1st year of Engineering courses. A sample of 737 1st year students of Engineering courses in a Portuguese public university was considered. Students age ranging from 16 to 41 years old (M = 18.29, SD =2.01), having entered the university with a GPA ranging from 154.6 to 200.0 (M =154.60, SD=18.10). Two measures of academic achievement were taken into account in this study: the number of subjects/curricular units successfully completed at the end of the 1st semester, and the average of the marks obtained in the approved curricular units at the end of the 1st year. In terms of predictors, we considered sex, age, parentś academic education, course and university option and access GPA. The regression analysis shows that GPA and sex have a higher impact on academic achievement. These data deserve attention from institutions and professors since some students access their courses without the necessary level of academic competencies, motivation and vocational career definition which justifies the need for diagnosing and levelling students' background and personal characteristics. It is also important because several students are not in their first option institution/course, which also impacts their adjustment process to the University., This work is financed by Project UID/CTM/00264/2019 of 2C2T – Centro de Ciência e Tecnologia Têxtil, and by CIEd–Research Centre on Education, project UID/CED/01661/2019, Institute of Education, University of Minho, through national funds of FCT/MCTES-PT.

The big problem of retired or drop out students is about academic achievement. The educational institution needs to follow up the advising system since advisers should guide the planning curriculum to their advisees. The data mining techniques are applied to anaylze the performance of the students and to impart the quality of education in the educational institutions. This paper focuses on classification models for applying in Education Data Mining. The classification models are applied to identify the suitable subject to the science students. The experiment is set to improve the student performance which comparing the performance of five classification models and then predicting the appropriated academic achievement in each major. To examine the experiment, we used 17,875 academic achievements within 483 students. Four measures; precision, recall, f-measure, and accuracy are evaluated the models. For the result, the best accuracy is Gradient Boosted Trees: GBT at 92.41% and F-measure value equals to 84.59%.

Mind map is used as an effective tool for teaching-learning in the classroom. With the growing demand of shifting from teacher-centric to learner –centric classroom, mind map is looked at as an active learning module to tap lateral thinking of students. It helps them to record notes in a non-linear way and promotes out-of-the-box thinking. Hence, mind maps are a popular tool to structure and visualize information. It is an activist learning method for capturing ideas on a horizontal surface. It is a graphical way to represent large information into a useful knowledge base and also helps in connecting the knowledge base with real time scenario. This facilitates the students to build better and new ideas. This paper aims at bringing out the learning experiences of students using mind map in recalling technical concepts in the course Materials and Metallurgy. A questionnaire was administered to find out the various learning experiences of third year Mechanical students of this course. Findings showed that there was a significant positive difference in student's academic achievement and attitude towards learning the subject through mind mapping.

Emotion regulation is how people manage their emotions especially moderate negative emotions such as anxiety, anger, and frustration. Having proper emotion regulation skills is critical to health, academic achievement, and work performance. In order to facilitate participants to manage emotions, we are developing a series of training programs by using HTC© ViveTM headset and Neuracle. The HTC Vive is to improve immersion in presence to lead to more effective training, and the Neuracle is using Electroencephalography (EEG) techniques for reading user's brainwave signals. In this paper, we focused on analyzing if emotion, which was reflected in brainwave signals, had changes when participants were exposed to positive/negative stimuli. The testing results indicated that there were noticeable changes in brainwave signals to stimuli. Therefore, it is reasonable to use brainwave signals as real-time input in our game development for improve emotion regulation skills in the future.

This work-in-progress research paper uses multiple regression of both cognitive and non-cognitive factors to model current GPA of engineering and computer science students. High school GPA and ACT/SAT scores are among the most common scores used as admission criteria, which result in a relatively homogeneous engineering population. Prior research, however, shows that these scores do not account for the variance in GPA once these students start undergraduate studies. In this work, we explore how students’ cognitive (e.g., study skills, test performance, regulatory behaviors, etc.) and non-cognitive factors (e.g., identity, motivation, personality, etc.) predict student success in their engineering pathways. The data for this initial study comes from a pilot survey, deployed in the summer of 2017, of 490 engineering and computing students from two large, public institutions, one on the West Coast, the other in the Midwest. We used multiple linear regression to control for demographic variables while examining the predictive value of particular cognitive and non-cognitive factors for student academic achievement (i.e., GPA) in university. Our analysis shows, not surprisingly, that standardized test scores only explain a small portion of the variance of undergraduate GPA. Including non-cognitive and affective factors into a regression model produced a marked increase in the explained variance. Our work is novel in examining a constellation of possible factors that predict undergraduate student GPA, by combining both cognitive and non-cognitive factors as predictors. This analysis begins to unpack particular factors that have potential to predict GPA of engineering and computer science students.

Student's performance assessment tools had gone through a huge improvement throughout time and reached high levels of reliability and accuracy. According to several studies, situational interest measure can be used to assess and improve student's academic achievement. So far, there is a lack in defining objective measures for situational interest rather than depending on the subjective measures. In this paper, we propose the use of EMD-based feature to detect situational interest in classroom settings using EEG. Several subjective measures: Personal Interest (PI) test, Situational Interest (SI) test and knowledge tests were used to evaluate subjectively student's interest. EEG data were then preprocessed and decomposed using Empirical Mode Decomposition (EMD) and the resulted Intrinsic Mode Functions (IMFs) was ranked using t-test to select the most significant ones. Eight features out of the 64 features extracted, were then fed into KNN and SVM classifiers achieving high accuracy of 90% and 100% respectively using features from temporal regions.

Studies show that small group learning promotes greater academic achievement, enthusiasm for learning, and persistence, yet student-centered, collaborative instruction is less ubiquitous in engineering and science than in other disciplines. While many reasons exist for this, time and resources required to develop such activities comprise a primary barrier to incorporating small group learning activities in engineering and science classrooms. This paper presents the “Fidget car” – a low cost apparatus that illustrates many core systems and control theory concepts. The authors have developed a sequence of activities for an undergraduate control theory class to address time and resource requirements for small group learning. These activities are presented here. Only simple measurement instruments, such as a smart phone and volt meter, are required to conduct activities with the Fidget car.

This research is to find valuable information from a large number of students' grade by the powerful data analysis capabilities of data mining technology. Based on Clustering technology, mining and clustering students' previous course credit data could bring about a classification model and effective evaluation of the model, which could provide students with scientific and objective course guidance and recommendations, improving students' academic achievement. At the same time, combined with spss and clustering technology, a credit early warning system model is constructed in this paper.

The degree of students actively engaging in learning greatly affects their academic achievement, cognitive development as well as education quality. Assessing student engagement in blended learning environment could help instructors and administrators understanding the quality of student learning, improving instructional design, in order to enhance student engagement in this environment. Based on the analysis of characteristic of blended learning environment, pivotal measurement indicators of student engagement, and related student engagement instruments, this paper designs and constructs a student engagement scale framework of blended learning environment in higher education, including the initially developed item pool.

Learning styles reflect the behavior of students in determining preferences in learning situations. Students learn in various ways, such as by observing, listening, acting, reasoning, memorizing, and visualizing. Students also think in different ways, and critical thinking is a desirable competency in academic achievement. Critical thinking may be influenced by individual learning preferences. The learning styles and critical thinking skills of engineering students are factors that contribute to how they learn to achieve the learning outcome and to their readiness to enter the market. Existing technology and workplace trends require engineers who are equipped with critical thinking skills, such as solving problems, making decisions, and understanding of technical and engineering concepts for a successful future. Therefore, this paper discusses the analyses of the learning styles and critical thinking skills of 315 engineering students from Universiti Tun Hussein Onn Malaysia. The Index of Learning Styles was adopted to measure the learning styles of the students, whereas a questionnaire for critical thinking was used to determine the critical thinking skills of the students in four dimensions: analysis, evaluation, induction, and deduction. Result shows that the engineering students are more inclined to be visual than verbal learners and they have received moderate critical thinking scores in each dimension. The result also indicates that no relationship exists between the learning styles and critical thinking skills of the engineering students.

Multiple-choice (MC) question is an important form of test to assess the students' academic achievement, especially in the e-learning applications. However, the classical evaluation metrics on MC questions (such as the correctness ratio) only consider the correctness of the final selection but ignore the solving progress of the testee. In the existing literature, the eye-tracking based visual attention was studied to infer the testee's cognitive progress towards a specific MC question. However, there is little work on the visual attention based evaluation of one complete MC test. In this paper, we measure the eye movement data of a group of students in an online test, which consists of forty more MC questions. We divide the screen area into five AOIs (area of interests), including one for the question and four for the candidate options. The fixation duration as well as the gaze sequence on these AOIs are recorded and studied. In the case study on the most difficult question, we observe the great differences among the eye movement of the testees in different academic levels. A new metric, namely Visual-Attention-assisted Score (VAS), is proposed to assess the student's performance with the bias of his fixations on the correct options. Experiment results show that, this metric can reflect the difference of gaze movement of testees, and thus it is helpful for the teachers to infer the real level of the students' academic achievement.

Many scholarships have been routinely offered by the educational institutions including Syiah Kuala University. This university has extensively distributed academic achievement and Financial Aid scholarships. Generally, students who have submitted all requirement documents and applied to these scholarships will be selected by a scholarship committee. By using the scholarship committee preferences, the results obtained an amount of vagueness and subjective. The selection process is also too difficult and take so long time. In order to solve this problem, a decision support system (DSS) using Multiple Attribute Decision Making with Simple Additive Weighting Method was demonstrated by using a numerical example to determine the scholarship recipients. The method is able to display a list of the scholarship recipients by sorting the best alternative values from a number of alternative values in descending order. In this paper, we used ten scholarship recipient candidates as the alternatives and four scholarship requirements as the criteria or the attributes. The results obtained with finding the weight values of each attribute, then ranking the optimal score value of each alternative. Finally, this method can be used in this case study.