Methodology and Methods of Research on the State and Causes of Academic Failure in Modern Schoolchildren

In this article, the author reveals research methods for working with underperforming students. We conducted an electronic survey (in connection with the pandemic) of students in urban schools and their parents, and each question was analyzed. As diagnostic methods for studying the state and causes of academic failure in modern schoolchildren, the author used various methods: the method of theoretical analysis of scientific, pedagogical, psychological, managerial and methodological literature on the research problem, the method of empirical research, the diagnostic method, the method of pedagogical experiment (ascertaining, forming, control and their description). The study of the reasons for academic failure was carried out in three stages, which differed both substantively and procedurally. The first stage was devoted to a questionnaire survey of students and parents of students in order to identify their interest and participation in general in the upbringing and academic performance of the child. At the second stage, the students' color world analyzer was used. And the final, third stage, contains the application of tests of school anxiety to diagnose the socio-psychological climate. The purpose of the methodology is to identify the level of anxiety in adolescents, localized in three main planes: educational activity, relationships with peers and significance in the eyes of adults and self-image. After all, adolescence is still an insufficiently mature and insufficiently socially matured person; it is a person who is at a special stage in the formation of its most important features and qualities. This stage is the borderline between childhood and adulthood.

Culturally and Linguistically Diverse Students

The structure of American public schools has altered within the past 30 years due to receiving extensive number of linguistically diverse students. The fact that culturally and linguistically diverse (CALD) students often experience academic failure within the U.S. public schools creates a subgroup of students who cannot achieve their educational goals. The purpose of this study is to explore ideal practices in order to enhance teachers' and policy makers' perceptions and awareness on the unique needs of CALD students. The findings of this study reveal the fact that ineffective methods for teaching CALD students and short-term goal-oriented educational policies fall short of meeting the academic needs of CALD students, and this research offers a conceptual framework that could contribute to CALD students' intellectual growth through effective and constructive language learning practices.

Trend and gender-based association of the Bangladeshi student suicide during the COVID-19 pandemic: a GIS-based nationwide distribution

Background: Students are one of the most vulnerable groups to suicide. Before the COVID-19 pandemic, a Bangladeshi study was conducted assessing their suicide patterns regarding gender-based associations. But how has the pandemic changed the Bangladeshi students’ suicide patterns is not studied yet, which is investigated herein. Besides, for the first time, this study provides GIS-based distribution of suicide cases across the country’s administrative district. Methods: As Bangladesh has no suicide surveillance system, this study utilized media reporting suicide cases following the prior studies. A total of 127 students’ suicide cases from March 2020 to March 2021 were finally analyzed after eliminating the duplicate ones, and data were synthesized following the prior studies. Arc-GIS was also used to distribute the suicide cases across the administrative district. Results: Results revealed that female (72.4%; n = 92/127) was more prone to die by suicide than males. About 42.5% of the cases were aged between 14 and 18 years (mean age 16.44 ± 3.512 years). The most common method of suicide was hanging (79.5%; n = 101), whereas relationship complexities (15.7%), being emotional (12.6%), not getting the desired one (11%), conflict with a family member (9.4%), academic failure (9.4%), mental health problem (8.7%), sexual complexities (6.3%), scolded or forbidden by parents (3.9%) were the prominent suicide causalities. In respect to gender and suicide patterns, only the suicide stressor was significantly distributed, whereas the method of suicide was significantly associated with GIS-based distribution. However, a higher number of suicide cases was documented in the capital (i.e. Dhaka) and the northern region than in its surrounding districts. Conclusions: The findings reported herein are assumed to be helpful to identify the gender-based suicide patterns and suicide-prone regions in the time of the COVID-19 pandemic to initiate suicide prevention programs of the risky students.

Internet Addiction, Depression and Relation with Academic Failure in Students of Allied Medical Sciences, Semnan, Iran

Background: Internet addiction, which is a result of increasing inevitable use of the Internet and smart phones, causes discomfort and serious social and occupational problems, consequently that can lead to some mental disorders such as depression. On the other hand, depression and Internet addiction are factors affecting students' academic performance. Objective: This study aimed to investigate Internet addiction, depression and their relation with academic failure in students of Semnan Allied Medical Sciences. Methods: In this cross-sectional study, all students who were in the 3rd and higher semesters were examined. Three questionnaires (demographic, Beck Depression Inventory, and the Internet Addiction Test by Young) were used. The academic failure was assessed using the student's grade point average in the previous 3 semesters. Collected data was analyzed through descriptive and inferential statistics methods at significance level of 0.05. Results: 170 students participated in this study. The correlation between depression and grade point average changes was negative (r=-0.19) and significant (p=0.01). Moreover, a positive (r=0.39) and significant (p=0.01) correlation was observed between depression and Internet addiction scores. Binary logistic regression analysis also indicated that students' depression score (P=0.04, OR1.04, CI 95%=1-1.08) and sex (P=0.008, OR=0.37, CI 95% = 0.17-0.77) can predict academic failure. Conclusion: Due to the observation of Internet addiction and depression in the students and effects of these disorders on their academic performance, it is necessary to educate students and families, identify risk factors and provide solutions to deal with it.

Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system

Predicting students at risk of academic failure is valuable for higher education institutions to improve student performance. During the pandemic, with the transition to compulsory distance learning in higher education, it has become even more important to identify these students and make instructional interventions to avoid leaving them behind. This goal can be achieved by new data mining techniques and machine learning methods. This study took both the synchronous and asynchronous activity characteristics of students into account to identify students at risk of academic failure during the pandemic. Additionally, this study proposes an optimal ensemble model predicting students at risk using a combination of relevant machine learning algorithms. Performances of over two thousand university students were predicted with an ensemble model in terms of gender, degree, number of downloaded lecture notes and course materials, total time spent in online sessions, number of attendances, and quiz score. Asynchronous learning activities were found more determinant than synchronous ones. The proposed ensemble model made a good prediction with a specificity of 90.34%. Thus, practitioners are suggested to monitor and organize training activities accordingly.

Exploring the Key Factors Influencing the Academic Performance of First Year Students at Undergraduate Level in Bangladesh

Since the number of students entering into the higher education system is increasing along with the dropout rates, therefore it is important for the institutions to identify the reasons that impact students’ academic performance in order to introduce the provision for necessary support for the students. This study is stimulated by the demand to determine such factors at undergraduate level that cause academic failure and dropout rates. Therefore, this study attempts to investigate what students perceive as the key influential factor the effects the academic performance of first year undergraduate students at university level. A quantitative research approach was followed to conduct the study. A survey was designed with questionnaires and was administered. Total 450 first year students, both from public and private universities in Bangladesh, were selected by convenience and stratified simple random sampling. The findings of this study disclosed that appropriate choice of course of study; students’ interest in the subject; regular attendance at lectures; timely and regular examination preparation; teachers’ pedagogical knowledge and skills; effective written communications skills; effective study methods are the topmost success factors that influence students’ academic performance. Oppositely, lack of interest in the course content; inadequate or poor exam preparation; irregular attendance at lectures/tutorials; late submission of assignments; lack of self-discipline, self-motivation and confidence; inability to distinguish between important and unimportant information; heavy course workload; inefficient time management reverse the academic performance of the students.

RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance

Predicting students’ performance is one of the most concerned issues in education data mining (EDM), which has received more and more attentions. Feature selection is the key step to build prediction model of students’ performance, which can improve the accuracy of prediction and help to identify factors that have significant impact on students’ performance. In this paper, a hybrid feature selection method named rank and heuristic (RnkHEU) was proposed. This novel feature selection method generates the set of candidate features by scoring and ranking firstly and then uses heuristic method to generate the final results. The experimental results show that the four major evaluation criteria have similar performance in predicting students’ performance, and the heuristic search strategy can significantly improve the accuracy of prediction compared with forward search method. Because the proposed RnkHEU integrates ranking-based forward and heuristic search, it can further improve the accuracy of predicting students’ performance with commonly used classifiers about 10% and improve the precision of predicting students’ academic failure by up to 45%.

Effects of a School-Based Intervention for Preventing Substance Use among Adolescents at Risk of Academic Failure: A Pilot Study of the Reasoning and Rehabilitation V2 Program

Tobacco, alcohol and cannabis use are important health problems that contribute greatly to causes of death in worldwide. Early onset of substance use increases rapidly during adolescence and it has been associated with a wide range of adverse events. Because substance use is associated with dramatic consequences, delaying the initiation of substance use among adolescents remains a major public priority. This study examined the effectiveness of a school-based intervention program based on the application of the Reasoning and Rehabilitation V2 (R&R2) program for preventing substance use among adolescents at risk of academic failure. A sample of 142 participants (aged 13–17 years old) who were studying alternative education provision in Spain were randomly allocated to two conditions (68 experimental group, 74 control group). A pre-test survey assessing past and current substance use was conducted prior the implementation of the program, while a post-test survey was conducted about 12 months after the pre-test. The results showed a significant effect of the R&R program in the reduction of cigarette smoking, episodes of drunkenness, alcohol consumption and cannabis use. However, for daily smoking and episodes of drunkenness such benefits showed a reduction over time. These findings offer additional evidence of the effectiveness of the Reasoning and Rehabilitation V2 program in Spanish adolescent students who are exposed to substance use and suggest areas of future research.

PENERIMAAN DIRI DARI KEGAGALAN AKADEMIK PEREMPUAN PERFEKSIONISME

This research aims to describe how academic perfectionism women rise from failure. This research uses a qualitative method with a case study approach. The number of participants in this study was four people obtained by purposive sampling technique with the characteristics of middle-aged adult women, the type of perfection in completing academic tasks, had experienced academic failure, and were willing to become research participants. In-depth interviews and observations collect data. This study indicates that educated perfectionist women always try to achieve maximum results io doing academic work. They also always have high targets that must be completed in their academic life. When they experience academic failures, at first, they tend to be sad, but after that, they can get up quickly. That is because they have a good self-concept of achievement, emotional maturity, belief in God's destiny, and a sense of optimism in themselves, and they also get reinforced motivation through social support from close relatives.

Academic Failure at University and Data Processing Methods Based on Decision Trees and Neural Networks: Research Methodology

For a long time, academic failure among university students sparked heated controversy. Many educational psychologists try to figure it out and then explain it. Statisticians have tried to predict it. Our research (article) aims to classify students into several categories, as well as to use the decision tree and artificial neural networks to classify first-year students and identify variables that may explain the problem.