From boring to scoring – a collaborative serious game for learning and practicing mathematical logic for computer science education

This study aims to determine the understanding of students' mathematical concepts taught using the Student Teams Achievement Division (STAD) with a problem-based learning (PBM) approach with a heuristic approach; the effect of STAD learning with the PBM approach on students' understanding of mathematical concepts in terms of high and low Initial Mathematical Ability (KAM); the effect of STAD learning with a heuristic approach on students' understanding of mathematical concepts in terms of high and low KAM; the effect of the interaction between STAD learning (with the PBM approach and the heuristic approach) and the initial mathematical ability (high KAM and low KAM) on students' understanding of mathematical concepts. This research was conducted at SDN 112233 Sidorejo, Labuhanbatu Selatan Regency from October to December 2019. A sample of 44 people was taken by random sampling. The instruments for collecting data were the students' mathematical concept understanding test and the KAM test. The data analysis used the one way annova test, simple regression test, and two way annova test. The results of the analysis show that the students' understanding of mathematical concepts taught using STAD with the PBM approach is higher than that of the STAD with a heuristic approach; There is a positive effect of STAD learning with the PBM approach on students' understanding of mathematical concepts in terms of high and low KAM; there is a positive effect of STAD learning with a heuristic approach on students' understanding of mathematical concepts in terms of high and low KAM; There is a significant interaction effect between STAD learning (with the PBM approach and with the heuristic approach) and the initial mathematical ability (high KAM and low KAM) on students' understanding of mathematical concepts.

Background: The Austrian State medical universities had to change their admission system in 2005. Until this year admission to medical studies was unrestricted. Innsbruck Medical University chose the Eignungstest für das Medizinstudium in der Schweiz (EMS) aptitude test for admission testing.Aims and objectives: Did the implementation of a selection process affect the academic performance and drop-out rates of students according to gender?Methods: Two groups of students were compared: ‘open admission’ (2002–2004), and ‘selected’ (2006–2009). Academic performance was tested according to results in the final exams after year 1 (SIP 1; SIP, summative integrierte Prüfung) and after year 3 (SIP 3). Drop-out rates were recorded using the registration system of the university.Results: Both, male and female ‘selected’ students had a higher passing rate regarding SIP 1 and they passed SIP 1 in shorter time and using fewer attempts than the open admission group. The percentage in passing SIP 3 did not change due to change in admission. The drop-out rates were significantly reduced for male and female students. ‘Unselected’ female students had a significantly higher drop-out ratio than ‘unselected’ male counterparts. After EMS testing, the drop-out ratios of female and male students were not significantly different.Conclusion: Selected applicants were more able and better motivated to study medicine.

Education disparities across regions often reflect differences in school density, teacher availability, and student dropout rates. This study aims to classifies junior high school education in West Java into more homogeneous groups to better understand these disparities. Two clustering approaches were applied: quartile grouping and the K-Means algorithm. Quartile grouping provided a simple categorization of each indicator into four levels (very high, high, low, very low), while K-Means offers a more flexible and data-driven segmentation. K-Means algorithm produced three distinct clusters: (1) Balanced and Stable regions with proportional ratios and low dropout rates, (2) High-Density but Stable regions concentrated in urban and periurban areas with high student-teacher and student-school ratios but controlled dropout levels, and (3) Elevated Dropout Risk regions, mostly in rural and southern areas, with lower density but higher dropout rates. The comparison shows that quartile grouping is easy to interpret for individual indicators, while K-Means provides more comprehensive insights into multidimensional patterns. This research highlights the potential of clustering methods to guide policymakers in designing differentiated strategies, from infrastructure expansion in dense regions to social support programs in dropout-prone areas.

The study examines the association between academic management and student dropout rates at a university in Lima, Peru, aiming to identify critical factors to enhance institutional effectiveness. It is grounded in key concepts such as curriculum planning, teaching-learning processes, comprehensive student support, and institutional resources, contextualized with previous research highlighting the importance of social and academic integration in reducing student attrition. A quantitative, non-experimental, correlational approach was utilized with a sample of 270 students, collecting data through Likert-scale-based questionnaires and analyzed using Spearman correlation and regression techniques. The results revealed a significant positive correlation (Rho=0.577, p<0.001) between effective academic management and lower dropout rates, indicating that curriculum planning, comprehensive support, and support resources are determinant factors. The discussion links these findings with previous studies and emphasizes the need to consider socioeconomic and emotional factors. The research provides practical guidelines for implementing university policies aimed at optimizing academic management and promoting student retention.

The hidden persistence of immigrant “dropouts”: Distortions, blank spots and blind spots in research on schooling careers

School dropout is a critical challenge for Brazilian education, having a multicausal nature and the potential to disrupt academic trajectories. This paper contributes to discussions on school dropout by covering primary and secondary educational levels. We conducted an extensive exploratory analysis of data provided by the National Institute of Educational Studies and Research Anísio Teixeira (Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira - INEP) from 1996 to 2022, considering factors such as government transitions and unforeseen events (e.g., financial crises and the COVID-19 pandemic). Thus, we handled millions of instances from the INEP database to explore data using Google Colab. We focused on the relationship between student dropout rates and various other factors, including categorical variables such as school location, type of administrative management, geographical region, and year, as well as numerical variables such as the number of classes, number of teachers, number of approved students, and number of failed students. Our goal was to identify which combination of these characteristics contributed most to the increase in student dropout rates. Our findings evidence that schools in urban areas of theNortheast region, whether municipal or state-run, have the highest student dropout rates in basic education in Brazil. The state of Alagoas shows a more pronounced duality in its education networks than the rest of Brazil, as the highest rates in elementary education occur in municipal schools. In contrast, state schools are the major contributors to high numbers of dropouts. Alagoas faces the most concerning dropout rates in high school education. The results showed periods of growth and decline in student dropout rates, aligning with government transitions, certain financial crises,and the COVID-19 pandemic. There is also a correlation between dropouts and failures in both elementary and high school education, with the correlation being much stronger in elementary education.

Dropouts are frequent in mental health care. Several client factors have been identified as dropout predictors, including ethnic minority status, race, low SES, and more severe symptoms. Research on therapist and process variables is less common, and findings are inconsistent. This study used administrative data for 434,317 patients from CIGNA Behavioral Health (CIGNA) to examine dropout rates by profession of provider, therapy modality, and DSM-IV diagnosis. Results indicate that among the providers, MFTs have the lowest dropout rates in the CIGNA network. Of the therapy modalities, individual therapy is associated with lower dropout rates than family therapy. Mood and anxiety disorders have lower dropout rates than other diagnosis categories, while schizophrenia, psychotic, and substance use disorders have the highest dropout rates.

The qualitative case study explores the effect of cost-sharing policy on the dropout rates of students in public secondary schools in the Limuru district, Kenya. Interviews were conducted with students who returned to school after they had raised money for tuition, teachers serving on the school district committee and the head teachers. The cost-sharing policy was viewed as a burden that has increased dropout, repetition and absenteeism in schools. Children from poor backgrounds continue to be marginalised as some national schools charge exorbitant school fees. Based on the findings the study offers some recommendations for policy-makers to consider.

Recent years have seen a significant increase in study interest in the areas of predicting student performance, avoiding failure, and identifying the variables affecting student dropout. One important indicator in online and open distance learning courses is the student dropout rate. We purpose the naive bayes classification method to construct the student dropout prediction using naive bayes. This work examines the critical topic of forecasting student dropout rates in higher education using machine learning approaches, with a particular emphasis on the random forest algorithm and the naive bayes algorithm. The study's goal is to properly anticipate dropout rates using data mining methods and machine learning algorithms after conducting a thorough evaluation of existing literature and approaches. The systematic method consists of data collection from a Kaggle dataset, data preparation to solve class imbalance via SMOTE oversampling, and algorithm selection. Random forest and naive Bayes approaches outperform other machine learning algorithms in terms of accuracy, sensitivity, specificity, and precision. The study underscores the importance of considering diverse factors such as demographic data, socioeconomic factors, and academic performance in dropout prediction models. The implications of this research extend beyond academia, with the potential to inform proactive interventions and support systems, ultimately leading to improved student outcomes and institutional effectiveness. According to this paper, the paper outputs that for the binary classification on the data set used in this project has best performed with Naive Bayes and Random Forest Algorithm with SMOTE oversampling. Keywords- SMOTE oversampling, machine learning, Random forest, naive bayes.

Generalized anxiety disorder (GAD), characterized by pervasive and highly distressing anxiety and worries, is associated with severe impairment. Although numerous agents from various drug classes are available to treat GAD, as many as 50% of patients have inadequate response, constituting an important medical frontier. In the face of this challenge, new pharmacological alternatives need to be further studied aiming at clinical improvement and better quality of life for patients. To assess the efficacy of pregabalin (PGB) compared with placebo for amelioration of anxiety symptoms in patients with GAD. A systematic literature search was performed using databases such as MEDLINE and EMBASE and other sources. The main outcome was Hedges' g for continuous scores. We used a random-effects model. Heterogeneity was evaluated with the I (moderate heterogeneity was assumed if I was >50% and high heterogeneity if I was >75%) and the χ-test (P<0.10 for heterogeneity). Publication bias was evaluated using the funnel plot. Meta-regression was performed using the random-effects model. For safety evaluation, we compared patients' dropout rates. We included eight randomized-controlled trials (n=2299) in our study, comparing the use of PGB in different dosages and placebo. In terms of the main outcome, PGB was found to be superior to the placebo group (Hedges' g=0.37; 95% confidence interval 0.30-0.44). The funnel plot assessment showed a low risk of publication bias. Between-study heterogeneity was not significant (I=0%), strengthening our results. Meta-regression showed no particular influence of any variable on the results. A categorical analysis of safety, using dropout as the most severe possible outcome, was carried out. No difference between PGB and placebo groups was observed in terms of the dropout rates. PGB was superior to placebo for the amelioration of GAD symptoms. In addition, the dropout rate was not significantly higher than that of the placebo groups. PGB was comparable to benzodiazepines in clinical response, but had lower dropout rates than benzodiazepine.

BackgroundRecruitment is one of the most serious challenges in performing randomized controlled trials. Often clinical trials with participants diagnosed with schizophrenia are terminated prematurely because of recruitment challenges resulting in a considerable waste of resources in the form of time, funding, and the participants’ efforts. Dropout rates in schizophrenia trials are also high.Recruitment challenges are often due to patients not wanting to participate in research but can also be due to clinicians’ concerns regarding individuals diagnosed with schizophrenia as participants in research. This paper reports how overcoming recruitment challenges not related to patients revealed high readiness to take part and low dropout rates in a one year long randomized controlled trial testing Guided Self-Determination (GSD) among outpatients with schizophrenia receiving treatment in Assertive Outreach Teams in the northern part of Denmark.MethodsGSD is a shared decision-making and mutual problem-solving method using reflection sheets, which was developed in diabetes care and adjusted for this study and utilized by patients with schizophrenia. Descriptive data on strategies to overcome recruitment challenges were derived from notes and observations made during the randomized controlled trial testing of GSD in six outpatient teams.ResultsThree types of recruitment challenges not related to patients were identified and met during the trial: 1) organizational challenges, 2) challenges with finding eligible participants and 3) challenges with having professionals invite patients to participate. These challenges were overcome through: 1) extension of time, 2) expansion of the clinical recruitment area and 3) encouragement of professionals to invite patients to the study. Through overcoming these challenges, we identified a remarkably high patient-readiness to take part (101 of 120 asked accepted) and a low dropout rate (8%).ConclusionDistinction between recruitment challenges was important in discovering the readiness among patients with schizophrenia to take part in and complete a trial with the GSD-intervention.

Multiple studies show student dropout rates across rich, low and middle-income countries. With the development of machine learning, various learning institutions have initiated predictive analytics algorithms to solve this issue. However, there is limited knowledge on how this strategy has been employed to optimize retention rates. The research aims to comprehensively review PAAs Strategies and demonstrate how they have been effectively implemented in optimizing retention rates with a case study focusing on North America. The study employed a quantitative research methodology and thematic study design to review the existing literature. The result shows that learning institutions used a combination of data mining classifiers such as k-Nearest Neighbor, Neural Networks, Decision Tree, and Naive Bayes to categorize student dropout rates and optimize retention rates. The PAAs approach has been utilized by various educational institutions across North America, applying multiple predicting algorithms to ensure efficiency. Keywords: Predictive analytics algorithms, mitigating dropout rates, retention rates, classifiers, Learning institutions, North America

PurposeThis study examines the differential effects of public education spending across countries with low, median and high enrolment and dropout rates at pre-primary and primary schools.Design/methodology/approachWe use panel data from 74 low- and middle-income countries spanning 1990 to 2021. Modelling school enrolment and dropout rates as linear functions of public spending and a host of other socioeconomic indicators, we run regressions with pooled OLS, two-stage least squares (2SLS) and the quantile regression approach. We choose quantile regression because it can better capture the varying effects of public spending across countries at different levels of enrolments and dropout rates.FindingsOur results show that public spending is significant in median and high-enrolment countries but is insignificant in low-enrolment countries at the pre-primary level. Public spending influences primary school enrolment across all categories of enrolments substantially. The effect of public spending is insignificant in countries with the lowest dropout rates.Originality/valueOur study makes three important contributions to the literature. Firstly, we revisit and provide new empirical evidence on the question of whether public spending affects “average” school outcomes using a larger dataset over a longer period of time. Secondly, we go beyond these studies by exploring how the effect of public spending varies across countries with low, median and high enrolment and dropout rates, providing insights on not just whether money matters, but for whom it matters most. Thirdly, utilising the quantile regression approach also brings a unique methodological innovation to the literature.

The goal of this research is to implement a prototype online game learning management platform with high usability via a formative design approach. Two stages of usability evaluations, namely, cognitive walkthrough and heuristic evaluation, are applied to assess the prototype system, followed by a small-scale survey. A total of 15 suggestions are obtained; In general, the students adequately perceived the incremental functionalities on real estate transactions and the learning concepts intended by this research. As a learning game, its design earned recognitions and raised user intention to use this platform as a learning tool. The overall user satisfaction on both the game and the class management platform ranges from neutral to slightly positive, thereby implying a positive general user attitude with more expectations for improvements. With this initial formative evaluation, the outcomes provide valuable input to subsequent formative design stages, as well as a foundation for high usability, closer to actual game scenarios, and a pioneering platform for informal game learning in higher education applications.

Despite the importance of selecting students whom are capable for medical education and to become a good doctor, not enough studies have been done in the category. This study focused on analysing the medical students' academic performance (grade point average, GPA) differences, flunk and dropout rates by admission types. From 2004 to 2010, we gathered 369 Konyang University College of Medicine's students admission data and analyzed the differences between admission method and academic achievement, differences in failure and dropout rates. Analysis of variance (ANOVA), ordinary least square, and logistic regression were used. The rolling students showed higher academic achievement from year 1 to 3 than regular students (p < 0.01). Using admission type variable as control variable in multiple regression model similar results were shown. But unlike the results of ANOVA, GPA differences by admission types were shown not only in lower academic years but also in year 6 (p < 0.01). From the regression analysis of flunk and dropout rate by admission types, regular admission type students showed higher drop out rate than the rolling ones which demonstrates admission types gives significant effect on flunk or dropout rates in medical students (p < 0.01). The rolling admissions type students tend to show lower flunk rate and dropout rates and perform better academically. This implies selecting students primarily by Korean College Scholastic Ability Test does not guarantee their academic success in medical education. Thus we suggest a more in-depth comprehensive method of selecting students that are appropriate to individual medical school's educational goal.