Predicting students’ outcomes from emotional response in the classroom and attendance

Ruiz, Sergio Escribano; Urretavizcaya, Maite; Rodríguez, Carlos Larrinaga; Fernández-Castro, Isabel

doi:10.1080/10494820.2018.1528282

Cited by 16 publications

(6 citation statements)

References 29 publications

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“…The relationship between related constructs supports the hypothesis that the two scales evaluate psychological processes with related but distinct dimensions (Cunha et al, 2016). Also, our findings contribute to expand investigations on specific academic anxiety which up to now has primarily focused on second language learners, and anxieties related to math and overall performance (Devine et al, 2018;Jalongo & Hirsh, 2010;Ruiz et al, 2018).…”

Section: Discussionsupporting

confidence: 80%

“…Given the important association between anxiety and academic achievement (Grills-Taquechel et al, 2012;Grills et al, 2014;Steinmayr, Crede, McElvany, & Wirthwein, 2016;Su et al, 2017), specific performance anxieties have been studied, such as math anxiety (Chang & Beilock, 2016;Devine, Hill, Carey, & Szűcs, 2018;Foley et al, 2017;Haase et al, 2012;Wood et al, 2012) and reading anxiety (Zbornik, 1988;Zbornik & Wallbrown, 1991). However, reading anxiety has been less explored than math or overall performance anxiety (Devine et al, 2018;Jalongo & Hirsh, 2010;Ruiz, Urretavizcaya, Rodríguez, & Fernández-Castro, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Translation and cross-cultural adaptation of the Brazilian version of the Reading Anxiety Scale: Short version

Piccolo

Giacomoni

Lima

et al. 2020

Estud. psicol. (Campinas)

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Reading anxiety refers to a negative emotional reaction to the reading process that causes the individual to avoid activities involving reading. To date, there are no instruments to evaluate this construct validated for Brazilian children. This study presents the cultural adaptation of the short version of the Reading Anxiety Scale and the study of its psychometric properties. The adaptation was conducted following standardized procedures: translation of the instrument into Brazilian Portuguese; synthesis of translated versions; evaluation by expert referees; evaluation of the instrument by the target audience; back-translation; pilot study with a clinical sample; preparation of the Reading Anxiety Scale short version; pilot study with typically developed children and the instrument psychometric properties. Factor analisys was used to reduce the number of items of the original scale. The studies of internal consistency and convergent validity suggest initial evidence of validity for the use of this instrument to investigate reading anxiety in Brazilian children.

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Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Translation and cross-cultural adaptation of the Brazilian version of the Reading Anxiety Scale: Short version

Piccolo

Giacomoni

Lima

et al. 2020

Estud. psicol. (Campinas)

View full text Add to dashboard Cite

show abstract

“…In addition to the background features and behaviours mentioned above, psychological characteristics are used to make predictions about course failure frequently (Sapiezynski et al, 2017;Ruiz et al, 2020), because research demonstrate that students' mindsets when studying determines their learning efficiency. Ruiz et al (2020) design experiments to explore the association between students' feedback about the emotions they feel in class and their academic performance. Sapiezynski et al (2017) collect psychological features of students through an online questionnaire and use them as features to make predictions.…”

Section: Psychological Featuresmentioning

confidence: 99%

Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

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Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field.

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“…Students' intrinsic and personality characteristics were collected by survey questionnaire approaches by Ruiz, S., Urretavizcaya, M., Rodrí guez, C., & Ferná ndez-Castro, I. which were not easily available in any database for the purpose of forecasting student performance. These researchers looked at how students' personality traits, learning styles and personalities, as well as their learning tactics and motivation facto influenced their academic success and overall well-being [11]. Using a brief questionnaire that included five different personality variables, as well as the student's learning style, psychological well-being, and educational achievement, Raga, R. C., & Raga, J. D. examined the impact of academic performance on psychological well-being [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Comparison of the Optimized Ensemble Model with Existing Classifier Models

Kumar¹,

Nidhi²,

Quteishat³

et al. 2022

IJMECS

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The purpose of this study is to conduct an empirical investigation and comparison of the effectiveness of various classifiers and ensembles of classifiers in predicting academic performance. The study will evaluate the performance and efficiency of ensemble techniques that employ several classifiers against the performance and efficiency of a single classifier. Reducing student attrition is a serious concern for educational institutions worldwide. Educators are looking for strategies to boost student retention and graduation rates. This is only achievable if at-risk students are appropriately recognized early on. However, most commonly used predictive models are inefficient and inaccurate due to intrinsic classifier limitations and the usage of minor factors. The study contributes to the body of knowledge by proposing the development of optimized ensemble learning model that can be used for improving academic performance prediction. Overall, the findings demonstrate that the approach of employing optimized ensemble learning (OEL) model approaches is extremely efficient and accurate in terms of predicting student performance and aiding in the identification of students who are in the fear of attrition.

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Predicting students’ outcomes from emotional response in the classroom and attendance

Cited by 16 publications

References 29 publications

Translation and cross-cultural adaptation of the Brazilian version of the Reading Anxiety Scale: Short version

Translation and cross-cultural adaptation of the Brazilian version of the Reading Anxiety Scale: Short version

Educational Anomaly Analytics: Features, Methods, and Challenges

Performance Comparison of the Optimized Ensemble Model with Existing Classifier Models

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