Research on Factors that Influence College Academic Performance: A Structural Equation Modelling Approach

Nursing students’ nomophobia, social media use, attention, motivation, and academic performance: A structural equation modeling approach

PurposeCreating suitable learning conditions in university classrooms continues to be a topical issue in higher education. The above means that a continued search for innovative teaching and learning strategies in universities remains a priority issue. The study therefore investigated conditions that facilitate the effective implementation of cooperative learning (CL) as well as how CL influences the academic performance of students in universities in Zimbabwe.Design/methodology/approachThe research adopted a quantitative research approach located in the descriptive research design. A sample of 341 undergraduate students selected using a simple random sampling strategy from a sample frame of 701 undergraduate students participated in the study. The study first used a pre-test and later, a post-test to determine the initial and end of semester (EOS) academic performance levels of students, respectively, on a Calculus 1 course. A self-constructed structured questionnaire was also used for data collection on the views of students towards the use of CL with particular reference to the 5 dimensions of positive interdependence (PDI), promotive interaction (PI), individual and group accountability (IGA), social and interpersonal skills (SS) and group processing (GP). The questionnaire was validated using confirmatory factor analysis. Structural equation modelling was used to test proposed relationships. A total of 341 questionnaires were administered through an email survey. Of the total, 149 completed questionnaires were received from the students, giving a return rate of 43.7%.FindingsThe results showed that positive interdependence, promotive interaction, social and interpersonal skills, individual and group accountability as well as group processing were important antecedents to the effective implementation of CL in universities. The results further showed that CL plays a significant role in improving the academic performance of university students; hence, academic performance was viewed as a significant consequence of CL.Research limitations/implicationsThis is the first study on the application of CL to enhance the academic performance of university students in the context of Zimbabwe. As a result, caution should be exercised when generalising the results as more studies to either confirm or disconfirm these results in the context of Zimbabwe may still be required.Practical implicationsThese results have implications on policy and practice with regards to active teaching strategies in universities. With regards to practice, the results demonstrated that the use of group activities that allow students to share knowledge and support each other while the teacher plays a facilitating role is important for enhancing students' academic performance. With regards to policy, the results showed that universities can enhance the academic performance of students if policies that promote student-centered teaching and learning approaches to ensure that teaching in universities is more student-led than lecturer-led are developed.Originality/valueWhile many studies have been conducted in other contexts, to establish the influence of CL on the academic performance of university students, there is no known study on the influence of CL that has been conducted in the context of Zimbabwean universities. The results of this study therefore are an eye opener on the role of CL in enhancing the academic performance of students in Zimbabwean universities.

Position effects may occur in both paper–pencil tests and computerized assessments when examinees respond to the same test items located in different positions on the test. To examine position effects in large-scale assessments, previous studies often used multilevel item response models within the generalized linear mixed modeling framework. Using the equivalence of the item response theory and binary factor analysis frameworks when modeling dichotomous item responses, this study introduces a structural equation modeling (SEM) approach that is capable of estimating various types of position effects. Using real data from a large-scale reading assessment, the SEM approach is demonstrated for investigating form, passage position, and item position effects for reading items. The results from a simulation study are also presented to evaluate the accuracy of the SEM approach in detecting item position effects. The implications of using the SEM approach are discussed in the context of large-scale assessments.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Zahayu Md Yusof, Masnita Misiran, Nur Syuhaida Mohamad Radzi, Asma Nabilah Sharifuddin, Noor Hannah Samsu Anuar, Muhamad Firdaus Ahmad, Afnan Aizzat Adnan; Evaluating factors affecting university students' academic performance by using structural equation model. AIP Conference Proceedings 8 February 2023; 2500 (1): 020022. https://doi.org/10.1063/5.0110765 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

Purpose This study used extended Unified Theory of Acceptance and Use of Technology (UTAUT) model to examine the effect of personal expectancy (PE), efforts expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), price value (PV) and habit (H) on behavioural intention (BI) to use Chat Generative Pre-Trained Transformer (ChatGPT) in higher education for an Indian context. The study also examined moderating effects of students’ self-innovativeness (SIN) and integrity (INT) on the relationship between BI and use behaviour (UB) for ChatGPT. Design/methodology/approach A sample of 311 students has been selected from Northern states of India by applying stratified proportionate random sampling method and four disciplines – engineering, business administration, science and fine arts were used as different strata in the sample selection process. Partial least squares structural equation modelling (PLS-SEM) approach has been used in data analysis to assess proposed theoretical model. Findings The study found PE, FC, PV and H significantly account for students’ BI and actual use of ChatGPT, whereas EE, SI and HM showed a negligible impact on BI. The BI was also found non-significant in predicting the usage behaviour for ChatGPT. The moderation effects of SIN and INT of students were also found non-significant on the relationship between BI and UB. Research limitations/implications A limited sample size of study and its focus on Northern states of India constrain the generalizability of findings to the other parts of world. Originality/value The study helps to understand the nuances associated with advanced artificial intelligence (AI)-driven tool such as ChatGPT in higher education for the application of best-practices. Therefore, this study aims to bridge this gap by examining determinants of ChatGPT usage with extended version of UTAUT model in Indian context by using additional constructs such as SIN and INT.

Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.

Gross manual dexterity is an important part of human factors and ergonomics. The purpose of the current study was to investigate factors affecting gross manual dexterity. Gender, palm length, grip strength, hand skin temperature, room temperature, and room humidity were analyzed simultaneously to predict gross manual dexterity by utilizing Structural Equation Modeling (SEM) approach. SEM showed that hand skin temperature was the most important factor for predicting gross manual dexterity, followed by several other factors such as grip strength, gender, and room temperature. In addition, room humidity, age, and hand size were also had significant indirect effects on gross manual dexterity. The SEM derived in this study could be a very valuable theoretical foundation which could be very beneficial for human factors engineer, physical therapist, and even medical doctors.

Factors affecting response actions of the 2020 Taal Volcano eruption among Filipinos in Luzon, Philippines: A structural equation modeling approach

Limitations record on a variable which have continuous variable changes make the 'time' attached to the data is discrete. That underlies development of continuous time model. Compared to discrete time model, the continuous time model has advantages that can make prediction at different lag. In addition, it still can perform continuous time model parameter estimation, although the objects observations do not always have the same time interval (irregular time). Thus, the purpose of this study is to examine the consistency of continuous time model coefficient parameter with structural equation model (SEM) approach in irregular time data. The method used is the Exact Discrete Model (EDM) with SEM approach. EDM is a model that connects the parameter to value which is underlying parameter of the continuous time model with non-linear relationships. The result analysis of the research indicated that the coefficients of continuous time model parameter are still consistent until 20% of irregular time with 5% consistency limits.

This paper aims to identify the major significant dimensions that contribute to the overall quality of life (QOL) of wage workers in Egypt. As the QOL is a complex, multidimensional, and interdisciplinary concept, forty-seven indicators under five domains were chosen to investigate the QOL determinants of wage workers in Egypt, namely job characteristics, job satisfaction, ICT access, gender equality and women empowerment, and neighborhood services and utilities. Using data from the 2018 wave of the Egypt Labor Market Panel Survey, the paper employed structural equation modelling (SEM) approach to investigate the impact of proposed dimensions on the overall QOL. In addition, multi-group SEM analysis was implemented to measure how people differ in the way they assess their QOL on the basis of various moderating variables such as the geographical region of residence, age group, and years of schooling. The findings showed the significant impact of the chosen dimensions on the overall QOL. Moreover, the multi-group models showed a significant variation among compared groups, in which the weights of dimensions vary due to the differences in socio-cultural characteristics and the surrounding environment, confirming the complexity of such a concept. As people usually differ in the way they assess their QOL, which is affected by their characteristics and priorities.

This paper concerns the divisibility of comprehension subskills measured in L2 listening and reading tests. Motivated by the administration of the new Web-based English as a Second Language Placement Exam (WB-ESLPE) at UCLA, this study addresses the following research questions: first, to what extent do the WB-ESLPE listening and reading items measure different comprehension subskills? Second, to what extent can L2 listening and reading be considered similar or different, with regard to the divisibility of comprehension subskills? Employing a structural equation modeling (SEM) approach, the factor structures of 110 test takers' scores on the WB-ESLPE listening and reading items were examined with regard to three types of subskills, that is, `understanding main/topical ideas', `understanding supporting/specific details', and `making inferences'. The results of the analyses indicated that the WB-ESLPE listening and reading items measure two or three kinds of subskills, and that while L2 listening and reading might share a common comprehension process, they may be distinct in the decoding processes involved due to the difference in mode of presentation. It is suggested that this divisibility of subskills in L2 comprehension tests might depend on the test takers' L2 proficiency as well as on the task characteristics of the test administered to them.

Public transportation in Phnom Penh, Cambodia has undergone significant changes in recent years, with daily ridership increasing from 7,000 at its reintroduction in 2014 to about 20,000 passengers by 2023. Despite this progress, a considerable portion of the buses operate at around 50% of their total capacity, presenting a challenge to the system’s efficiency and sustainability. In order to shift Phnom Penh towards a more sustainable transportation future, an examination of transit loyalty emerges as a strategy to retain and attract more users. This study aims to contribute to the understanding of urban public mobility in the context of a developing country, by investigating the relationships among accessibility, perceived value, satisfaction, service quality, and loyalty, as well as exploring the perceptions of loyal users of the public buses. Data collection was conducted through in-person and questionnaire-based interviews at bus stops and on the buses. Out of the 220 responses gathered, 186 were deemed suitable for further factor and reliability analyses. Finally, the data were analyzed using a Structural Equation Modelling (SEM) approach, and the best fitting model was developed to illustrate the relationship among the constructs of transit loyalty. Findings suggest that loyalty is primarily influenced by satisfaction, which is driven by perceived value and accessibility. Furthermore, service quality was found to have an impact on perceived value. This study suggests that there is a strong possibility for more demand for public transportation for non-bus users, provided the system offers better coverage and frequency. Consequently, this presents as an opportunity for policymakers and stakeholders to leverage these insights in shaping policies that would enhance the sustainability and appeal of public transportation in Phnom Penh.

The present study examined the factor structure of the University of Tehran English Proficiency Test (UTEPT) that aims to examine test takers’ knowledge of grammar, vocabulary, and reading comprehension. A Structural Equation Modelling (SEM) approach was used to analyse the responses of participants (N= 850) to a 2010 version of the test. A higher-order model was postulated to test if the underlying factor structure, obtained in a data-driven manner, corresponds with the proposed structure of the test. The results revealed an appropriate model fit with the data, pointing to the fact that the three sections of UTEPT, i.e., structure, vocabulary, and reading, and their sub-components, except for the restatement section of reading, are good indicators of written language proficiency as assessed by the UTEPT. It was also found that the three sections assess distinctive constructs. The findings suggest that UTEPT is a valid measure of the written language proficiency of Ph.D. applicants to University of Tehran.

PurposeThe study aims to propose a predictive model that combines personality and demographic factors to predict student academic performance (SAP). This research study works on understanding, enhancing and applying techniques to enhance the prediction of SAP.Design/methodology/approachThe authors gathered information from 305 university students from Al-Zintan University Libya. The study uses a survey questionnaire to collect data on essential variables. The purpose of the questionnaire is to discover variables that affect students' academic performance. The survey questionnaire has 44 closed questions with Likert scale designs that were distributed to a variety of college students at the start of the first semester of 2022. It includes questions about demographics, personality, employment and institutional aspects. The authors proposed a predictive model to identify the main fundamental components, consisting of one dependent variable (SAP) and five independent constructs. The suggested model is tested using partial least squares (PLS) and structural equation modeling (SEM), which perform better than covariance-based structural equation modeling (CB-SEM). PLS-SEM performs well with smaller sample sizes, even for complicated models.FindingsThe study results show that the proposed model accurately predicted the student's academic performance. The personality trait variables are a key factor that determines the actual student's academic performance. The student's academic performance is significantly impacted by each variable in the personality trait variables as well.Originality/valueThe process of validating research was done empirically through the accuracy and efficiency of model performance. The study differs from previous studies in that it accumulated a wide range of factors from different dimensions, including student demographics and personality trait factors. The authors developed a structural equation model to predict students' academic performance.

Effects of co-curricular activities on student's academic performance by machine learning