Assess College Students Research Articles

Personal Financial Management Skills Of University Students and Their Financial Experiences During The Covid-19 Pandemic

Abstract: Purpose: The main purpose of this study was to assess university students’ financial management skills and their financial experiences during the Covid-19 pandemic. Research methodology: The research adopted the explanatory sequential mixed methods research design where a survey of 181 students on work-related learning was conducted using questionnaires and qualitative interviews. Data were analyzed using IBM SPSS Statistic 27 Results: The study established that the majority of the students do not have a budget, run out of money before receiving the next income, have no funds set aside for uncertainties or medical expenses related to the Covid-19 pandemic, and to make ends meet most of the students borrow money from relatives and friends. The majority of students struggled financially because of the Covid-19 pandemic. Limitations: Questionnaires and interviews were only distributed/conducted online due to the Covid-19 pandemic. Contribution: Personal financial management skills are a vital human capital skill for students. Therefore the need to explore such skills and financial experiences of University students during the Covid-19 pandemic for the first time. Keywords: 1. Personal financial management skills 2. financial planning 3. financial experiences

Farmhouse Fumes: Assessing College Students’ Knowledge of Sustainable Protein Sources

Farmhouse Fumes: Assessing College Students’ Knowledge of Sustainable Protein Sources

Eating Behaviors and Body Image Perception among College Students

Objective: In this study, we assessed college students’ eating behaviors and determined the association between their eating behaviors and body image perception (feelings about body, shape, and weight). Methods: We surveyed college students (N = 184) enrolled at least parttime and actively living on campus during the 2019-2020 academic year. We used 4 validated surveys (EDE-Q, NEMS-P, SATAQ-3 and SATAQ-4) to collect all data. Two trained facilitators aided students in completing the surveys in the campus’s nutrition lab. We used weighted linear regression to assess the association between eating behaviors (frequency of fruit, vegetables, candy, and chips consumption available in dorm rooms, and meal consumption frequency at restaurants) (independent variables) and body image perception (dependent variable). We also calculated odds ratios. Results: We found statistically significant associations between students reducing their consumption of available fruit, vegetables, candy, soda, and chips in their dorm rooms to control weight and shape, choosing meals more frequently at fast-food versus sit-down restaurants, and unhappiness with weight and shape. Conclusion: Our results may assist college campus personnel in providing students with the necessary skills to develop better eating habits and improved body image. Students with a positive body image and a healthy relationship with food are more likely to have better mental and physical health.

Crazy About Caffeine: Assessing College Students Caffeinated Beverage Content Knowledge

Crazy About Caffeine: Assessing College Students Caffeinated Beverage Content Knowledge

Developing College students’ computational thinking multidimensional test based on Life Story situations

At present, research on computational thinking in universities is gaining interest, and more attention is being paid to the cultivation and teaching of computational thinking. However, there is a lack of computational thinking assessment tools for college students, which makes it difficult to understand the current status and development of their computational thinking. In this study, computational thinking is regarded as the ability to solve practical problems. By analyzing the relevant literature, we identified five dimensions of computational thinking – decomposition, generalization, abstraction, algorithm and evaluation – and described their operational definitions. Referring to the Bebras and the problem situations in Google computational thinking education, we set up a life-based situation that college students are familiar with. Based on the life story situation, we developed a multidimensional assessment for college students’ computational thinking. This assessment tool contains 14 items, all of which are multiple-choice questions, and the structure and quality of the tool are verified by multidimensional item response theory. The results show that the assessment tool has good internal validity and can discriminate different disciplines of college students. The college students’ computational thinking test developed in this study can be used as an effective tool to assess college students’ computational thinking.

Evaluation Model of College Students’ Mental Health Quality Based on Computational Intelligence

In order to realize the reliability evaluation of college students’ mental health quality, a model of college students’ mental health quality evaluation based on computational intelligence is proposed. The mutual information quantity is used as the benchmark parameter to measure the interaction between the two variables of college students’ mental health quality. The greedy algorithm to find the best feature subset for college students’ mental health quality assessment is designed. The feature sequence sampling and the reassembly model of college students’ mental health quality is constructed. The reliability assessment and nonparametric quantitative feature estimation are carried out, so as to complete the quantitative analysis and assessment of college students’ mental health quality. The test results show that this method has a high confidence level and good reliability. The evaluation results can accurately reflect the depression, stress, anxiety, and other emotions related to college students’ mental health, and have a good effect.

University students’ help seeking intention for depression from health professionals; a cross sectional study

ObjectivesTo assess University students’ intention to seek help for depression from health professional and associated factors among University of Gondar students, Northwest, Ethiopia.MethodsAn institution-based cross-sectional study was conducted among 487 students. Multistage sampling technique was used to select study participants. Self-administered questionnaire was employed to collect the data. General help seeking questionnaire with major depressive disorder case vignette was used to assess students’ intention to seek help for depression. The collected data was analyzed using SPSS version 20. Simple and multiple linear regression analysis were performed to identify factors for intention to seek help for depression. Association was described by using adjusted unstandardized β coefficient along with 95% confidence interval. Finally, P-values < 0.05 in adjusted analysis were taken as a cut off for significant association.ResultsThe mean score of intention to seek help from health professionals was 3.84 (±0.76)with a range of (1 “very unlikely” to 5 “very likely”). About 67.8% of the study participants would seek help from health professionals if they would have depressive symptoms which was depicted in the case vignette. In the multiple linear regression analyses, student age (unstandardized ß = 0.07, 95% CI: (0.05, 0.10)), good attitude towards seeking professional help (ß = 0.03, 95% CI: (0.02, 0.04)) were factors positively associated with intention to seek help.ConclusionThe current study showed that more than three-fifth of the study participants reported they were likely or very likely to seek help for depression from health professionals. Increased age and favorable attitude were factors associated with intention to seek help for depression. Working on awareness creation and attitude change towards depression would be necessary to increase students’ intention to seek help for depression from health professional.

College students’ intent to intervene with a hypothetical peer exhibiting depression: Leveraging lessons learned during the COVID-19 pandemic

This study assesses college students’ intent to intervene when presented with a hypothetical peer exhibiting depression in one of three scenarios: depression, sadness, and depression in quarantine during COVID-19. Using the Theory of Planned Behavior (TPB), variations in constructs associated with intent were examined by context (external triggers vs. no trigger), knowledge of, and experience with depression. One hundred and sixteen health sciences students read three vignettes and completed an enhanced TPB questionnaire. Intent to intervene was greater when the vignette target was experiencing depression with external stressors. Prior experience with depression and knowing someone with depression were associated with greater intent to connect the hypothetical peer to counseling resources regardless of vignette scenario. Due to increased mental health concerns resulting from the COVID-19 pandemic, efforts promoting awareness of mental illness in peers may benefit from increasing education about stressors and causes of depression that may not be observable.

Starting a New Business? Assessing University Students’ Intentions towards Digital Entrepreneurship in Saudi Arabia

The creation of new businesses is vital to the economic prosperity and stability of developed and developing countries worldwide. Although three-quarters of adults in Saudi Arabia have perceived good opportunities to start new businesses, the country has recently experienced poor entrepreneurial growth. This study aims to assess the factors that influence students’ intentions to start new ventures using technological platforms after completing their education. It analyses the existing alternative research models and validates them using data collected from 763 students at a Saudi public university. The same data are then used to validate the proposed research model, revealing that it outperforms all alternative models. The findings indicate that attitude, subjective norm, perceived feasibility, perceived desirability, propensity to act, digital entrepreneurial education, and innovativeness are constructs which exert a significant influence on entrepreneurial intentions, while perceived behavioural control and self-efficacy significantly influence entrepreneurial behaviour. The indirect effects of attitude, subjective norm, perceived desirability, perceived feasibility, propensity to act, innovation, and digital entrepreneurial education on entrepreneurial behaviour through entrepreneurial intentions are also found to be significant. This paper makes a number of theoretical contributions and has implications for practice and government.

Validation of a Speech Database for Assessing College Students' Physical Competence under the Concept of Physical Literacy.

This study developed a speech database for assessing one of the elements of physical literacy—physical competence. Thirty-one healthy and native Cantonese speakers were instructed to read a material aloud after various exercises. The speech database contained four types of speech, which were collected at rest and after three exercises of the Canadian Assessment of Physical Literacy 2nd Edition. To show the possibility of detecting each exercise state, a support vector machine (SVM) was trained on the acoustic features. Two speech feature sets, the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) and Computational Paralinguistics Challenge (ComParE), were utilized to perform speech signal processing. The results showed that the two stage four-class SVM were better than the stage one. The performances of both feature sets could achieve 70% accuracy (unweighted average recall (UAR)) in the three-class model after five-fold cross-validation. The UAR result of the resting and vigorous state on the two-class model running with the ComParE feature set was 97%, and the UAR of the resting and moderate state was 74%. This study introduced the process of constructing a speech database and a method that can achieve the short-time automatic classification of physical states. Future work on this corpus, including the prediction of the physical competence of young people, comparison of speech features with other age groups and further spectral analysis, are suggested.

Assessing College Student Knowledge and Health-Related Behaviors During the COVID-19 Pandemic

ObjectivesThe purpose of this study was to assess college students’ knowledge and awareness of COVID-19 risk factors for severe illness and to identify if students were changing their eating and physical activity behaviors. MethodsA 43-question retrospective survey was developed to examine knowledge and health habits before and during the COVID-19 pandemic. Undergraduate students from one small private school, one large public university, and one large community college completed the online survey. Demographic and knowledge variables were analyzed using frequencies while change in nutrition and physical activity habits were assessed using Wilcoxon Signed Rank tests through Jamovi 2.2.5 software. ResultsA total of 368 students (mean age = 23.9 ± 7.4 years, 68.8% female, 80.7% white) completed the survey. Results show that 97.3% of students were aware of the health consequences of poor nutrition and lack of physical activity during the pandemic. The majority of students (82.6%) were aware of increased risk of severe illness due to co-morbidities (i.e., cardiovascular disease, diabetes, and hypertension) during the pandemic, yet 67.8% reported making no changes to their daily activities to reduce that risk. There were significant reductions in light physical activity (n = 368, W = 9376, p < 0.001, effect size 0.47) and a significant increase in sedentary activity (n = 368, W = 3756, p < 0.001, effect size 0.53). When asked to reflect on their healthy eating habits during the pandemic, 35.7% of students reported no change, while 31.6% reported an increase and 32.7% reported a decrease. ConclusionsThese findings demonstrate that students are aware that having poor nutrition and lack of physical activity will increase their risk of developing a more severe case of COVID-19. Regardless, students report making no changes even with this awareness and knowledge. Time spent engaged in physical activity has declined during the pandemic, and sedentary behavior has increased. These findings may help inform future planning and care practices for college students as the pandemic continues. Funding SourcesStonehill College’s student undergraduate research (S.U.R.E) program funded this research.

Research on evaluation Standard of Physical Literacy ability based on cloud Model

In order to better solve the ambiguity and uncertainty in the evaluation indicators of college students' physical literacy, the cloud model method is used to evaluate college students' physical literacy ability, and the evaluation system is constructed by combining the first and second level indicators such as sports cognition, sports ability and sports behavior. With the cloud theory, the index weight cloud and standard cloud are set up Based on information of each evaluation index and algorithm to get the final evaluation index of cloud, finally compared with the evaluation standard of cloud, determine the advantages and disadvantages of indicators of physical quality evaluation standards can comprehensive scientific assessment of college students' sports literacy ability, found that college students' literacy ability of sports based on cloud model sports ability is good, sports awareness and sports behavior ability needs to be strengthened The method is scientific and the results are more intuitive with the help of evaluation cloud map, which is helpful to improve the research of physical literacy in China.

Evaluation of College Students’ Ideological and Political Education Management Based on Wireless Network and Artificial Intelligence with Big Data Technology

The construction of a correct worldview, outlook on life, and values for students is linked to the development and breakthrough in the management of ideological as well as political education of students. At the same time, college students must be encouraged to follow well-rounded education and struggle to be well-prepared for the challenges of the new era. In order to raise students’ understanding of the critical role that political and ideological education plays in their academic success, it is authoritative that efforts to integrate these two spheres of learning be extended and new encounters made. That’s what prompted this study, which is focused on assessing college students’ level of ideological and political education administration, and it uses a mixture of big data technologies as well as artificial intelligence (AI) to do it. The accuracy of the traditional ideological as well as political education management quality assessment algorithm is not high, feature information extracted by the single-scale neural network (NN) is not rich enough, and the multiscale convolutional network (CN) fusion cannot consider the different values and importance for each scale. In this paper, the convolution kernel of the two-dimensional CN is changed to a one-dimensional convolution kernel, and the multiscale feature fusion CN model MCNN is first designed. The model is optimized and improved, the attention mechanism is integrated, and the MACNN model for the management evaluation of ideological as well as political education is proposed. Besides, this work organizes the network model in a wireless network environment that users can contact and operate at any time.

Research on evaluation system of ideological and political education of college students based on decision system

Nowadays, the ideological and political (IAP) training of the college and university students is of a great interest for everyone due to the rapid change in the political issues and the race of super power. By analyzing the present states and research on the system of evaluating the IAP training of college students, it is obvious that the current evaluation systems are not satisfactory and have different deficiencies such as openness, transparency, and lack of supervision. The existing systems are pretty straightforward, and the assessment process is not open enough. The evaluation systems are not solid, and there are still many challenges for the evaluation system of IAP training of college students. Students can only accomplish a comparably fair and scientific assessment result by building an efficient and innovative decision-making system with the help of analyzing the evaluation system of the IAP education of the students. This allows the students to get to know each other more closely and better promote their overall growth. In this research work, we examined the present assessment systems of IAP education and presented a paper in order to emphasize its shortcomings and flaws such as openness, transparency, the lack of a distinct evaluation system, and supervision. Further, we examined the values of big data analytics in assessing college students’ IAP education and present a new evaluation method based on big data analytics. The new approach assures the results transparency and openness, and hence, the evaluation system is assessed more accurately. Based on the experimental and evaluation results, it is recommended that the college teachers should understand the value of big data, and a separate platform needs to be developed through which different institutions may exchange data about the assessment system.

Undergraduate Surveys Reveal That Instructors Are Key in Students Overcoming Classroom Struggles During the COVID-19 Pandemic

Many undergraduate students encounter struggle as they navigate academic, financial, and social contexts of higher education. The transition to emergency online instruction during the Spring of 2020 due to the COVID-19 pandemic exacerbated these struggles. To assess college students’ struggles during the transition to online learning in undergraduate biology courses, we surveyed a diverse collection of students (n = 238) at an R2 research institution in the Southeastern United States. Students were asked if they encountered struggles and whether they were able to overcome them. Based on how students responded, they were asked to elaborate on (1) how they persevered without struggle, (2) how they were able to overcome their struggles, or (3) what barriers they encountered that did not allow them to overcome their struggles. Each open-ended response was thematically coded to address salient patterns in students’ ability to either persevere or overcome their struggle. We found that during the transition to remote learning, 67% of students experienced struggle. The most reported struggles included: shifts in class format, effective study habits, time management, and increased external commitments. Approximately, 83% of those struggling students were able to overcome their struggle, most often citing their instructor’s support and resources offered during the transition as reasons for their success. Students also cited changes in study habits, and increased confidence or belief that they could excel within the course as ways in which they overcame their struggles. Overall, we found no link between struggles in the classroom and any demographic variables we measured, which included race/ethnicity, gender expression, first-generation college students, transfer student status, and commuter student status. Our results highlight the critical role that instructors play in supporting student learning during these uncertain times by promoting student self-efficacy and positive-growth mindset, providing students with the resources they need to succeed, and creating a supportive and transparent learning environment.

The reliability and validity of a Chinese version of the Student Career Construction Inventory

Although the Student Career Construction Inventory (SCCI) has been used in many countries to measure the career adapting responses of college students, the applicability of the SCCI for use with Chinese college students has not yet been tested. Thus, we analyzed data from 411 college students who completed the SCCI and other related scales. The results support the second-order factor structure of the SCCI, and we found that the four dimensions, and the SCCI as a whole, were reliable. Students who scored higher on the SCCI scored higher on measures of career exploration, career planning, and career engagement, supporting the high congruent validity of the SCCI. Students with higher SCCI scores also showed stronger vocational identity and higher academic achievement, supporting the high criterion validity of the measure. Finally, the SCCI demonstrated scalar invariance across genders. Generally, the results show that the Chinese version of the SCCI is a valid and reliable scale for assessing college students' career adaptation thoughts and behaviors.

Assessing knowledge of and attitudes towards plagiarism and ability to recognize plagiaristic writing among university students in Rwanda.

Plagiarism is a serious type of scholastic misconduct. In Rwanda, no research has been conducted to assess university students’ attitudes and knowledge of plagiarism and if they have the skills to avoid plagiarizing. This study was conducted to assess knowledge of and attitudes towards plagiarism, as well as ability to recognize plagiaristic writing, among university students in Rwanda. An online questionnaire containing 10 knowledge questions, 10 attitude statements, and 5 writing cases with excerpts to test identification of plagiarism was administered between February and April 2021. Out of the 330 university students from 40 universities who completed the survey, 75.8% had a high knowledge level (score ≥ 80%), but only 11.6% had a high score in recognizing plagiaristic writing (score ≥ 80%). There was no statistically significant association between knowledge level and ability to recognize plagiaristic writing (P = 0.109). Lower odds were found in both diploma/certificate and bachelor students of having high knowledge as well as of having high ability to recognize plagiaristic writing than in master’s students. Although respondents generally disapproved of plagiarism, approximately half of the respondents indicated that sometimes plagiarism is unavoidable, and self-plagiarism should not be punished in the same way as plagiarism of others’ work. Inter-collegial collaboration on effective plagiarism policies and training programs is needed.

Assessment of College Students in Understanding Beliefs, Behaviors, and Key Levels of Influence regarding COVID-19 Vaccine Uptake

Assessment of College Students in Understanding Beliefs, Behaviors, and Key Levels of Influence regarding COVID-19 Vaccine Uptake

Risk of food and housing insecurity among college students during the COVID-19 pandemic.

The aim of this study was to assess college students’ food and housing insecurity risk amidst the pandemic. Data were collected through an online survey in the summer of 2020 from 1956 graduate and undergraduate students attending a large, private, urban university in the Midwest, U.S. Food insecurity among students increased (25% before; 29% during COVID) with housing insecurity staying roughly the same (34% before; 36% during COVID). Results indicate certain student groups were at greater risk of basic needs insecurity during the pandemic compared to their counterparts. Results also suggest changes in food and housing insecurity trends. College students are burdened with basic needs insecurity, exacerbated during the pandemic. Institutions need to work toward solutions to address the needs of vulnerable populations disproportionately affected by basic needs insecurity. Recommendations on addressing the basic needs of college students are also provided.

Sportsman’s Mental State Evaluation and Early Warning Method Based on Intelligent CNN

Aiming at the intelligent needs of psychological state assessment of university students, the text information-based psychological problem identification approach is investigated in the paper. This approach uses the text of student forums within universities as the database and introduces the convolutional neural network (CNN) model in deep learning, which contains a convolutional layer, a pooling layer, and a fully connected layer. After the convolution is completed, the convolution result is delinearized by the activation function, and then, pooling is performed to improve the fitting ability of the network for nonlinearities. For data processing, behavioral features, attribute features, content features, and social relationship features are extracted from text information as the input of the CNN. The psychological lexicon of expertise (LIWC) is used to enhance the efficiency of text word frequency statistics when performing text content extraction. To evaluate the performance of the proposed method, simulations are performed in the open dataset of CLPsyh2017 ReachOut Forum, and the FastText method is used as a comparison. The results show that the CNN model achieves an accuracy of 0.71 in the full-sample domain, which is significantly higher than that of the FastText model at 0.64. In the early warning evaluation of mental states, the CNN performance is better than that of FastText.