Factors Determining the Effectiveness of Secondary Schools in Nigeria

Ajayi, Iyiade Adeseye; Ekundayo, Haastrup T.

doi:10.1080/09720073.2011.11891174

Cited by 7 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the sample size of public schools is higher than the private schools, the effects are equaled by the average or the mean. Another explanation could be that the teachers in the public schools did not monitor the students/pupils effectively during the period of questionnaire administration or the students in the private schools understand the questionnaire better than those in the public schools [147]. As mentioned earlier, the workload of teachers in public schools could be culpable [148].…”

Section: School Type and The Trio Of Aggression Delinquency And Hyperactivitymentioning

confidence: 98%

Statistical analysis of childhood and early adolescent externalizing behaviors in a middle low income country

Bishop

Okagbue

Odukoya

2020

Heliyon

View full text Add to dashboard Cite

The article show the pattern of externalizing behavior across age, gender, school type, and school level, with reference to aggression, delinquency, and hyperactivity. The study samples were primary school pupils and secondary school students from three selected Local Government Areas (LGA) in Ogun State, Nigeria [Ado-Odo/ Ota, Ifo, and Yewa South]. Their ages ranged from 10 to 20 years. The student/pupil sample was 1770 in all. The instrument used was an adapted version of Achenbach's child behavior checklist and youth self-report. Basic descriptive statistics like frequency, percentage, mean, standard deviation, as well as non-parametric statistics like Phi-coefficient, Chi-square, Goodman and Kruskal's gamma, Mann Whitney U test and Kruskal Wallis H test were utilized. Inferential parametric statistics like Pearson r, analysis of variance and simple regression were also utilized. Four major findings were reported. Firstly, the private schools irrespective of age, gender and level, scored higher than the public school in aggression, delinquency, and hyperactivity. Secondly, aggression is higher in secondary schools, while delinquency and hyperactivity are more prevalent in primary schools. Thirdly, school level and school type are the strongest predictors of externalizing behavior. Lastly, correspondence analysis showed a similar behavioral pattern for the three behaviors and three distinct behavioral patterns. i). Respondents aged 10 and below and those in primary schools (ii). Male, public and between 16 and 20. iii). Private, secondary, female and between 11 and 15. Implications of the study are discussed.

show abstract

Section: School Type and The Trio Of Aggression Delinquency And Hyperactivitymentioning

confidence: 98%

Statistical analysis of childhood and early adolescent externalizing behaviors in a middle low income country

Bishop

Okagbue

Odukoya

2020

Heliyon

View full text Add to dashboard Cite

show abstract

“…Furthermore, teachers should give feed back for students' learning achievement. A conducive circumtances enable students to improve their ability in delivering information effectively as well as to advance their learning outcomes (Ajayi & Ekundayo, 2011;Liu, Lin, Jian, & Liou, 2012).…”

Section: Resultsmentioning

confidence: 99%

Profiling students’ environmental care attitudes taught using Sasak Tribe local wisdom-integrated model

Hunaepi¹,

Dewi²,

Sumarjan³

2019

JPBI

View full text Add to dashboard Cite

Sasak Tribe possesses unique local wisdom which is potential to be utilized in term of improving students' care attitudes toward the environment. This study aimed at profiling students' evironmental attitudes who were taught using Sasak Tribe local wisdom-integrated model. This descriptive research was designed with a one-shot case study. The sample used in this study was 140 VII graders chosen using random sampling technique. The sample comprised of 3 classes of SMPN 2 Gunung Sari and 3 classes of SMPN 3 Lingsar placed in West Nusa Tenggara-Indonesia. The data analysis technique used was descriptive statistics in term of central tendency. The measured indicators were environmental awareness (EA), developing empathy (DE), and effect handling (EH). The results showed that the students live in suburb area tended to have better environmental care attitudes, in which the mean values were 86.66 (EA), 75.69 (DE), and 42.66 (EH) compared to those who live in urban area in which the mean values were 68.73 (EA), 57.07 (DE), and 30.62 (EH). Based on this findings, further evaluation in several aspects should be done.

show abstract

“…In this scenario, this research contributes to identifying parameters to better guide work plans and developed contents, aiming at better performances concerning the teaching-learning process directed at students, and, consequently, better external evaluation in IDEB results. Since most researchers present a generic point of view, employing only descriptive and/or inferential statistics to verify certain decisive characteristics of the educational development process [8,9], there are few studies employing advanced machine learning techniques that can analyze many attributes to more accurately predict variables in order to positively contribute to students' academic performance [10].…”

Section: -Introductionmentioning

confidence: 99%

The Role of Technology in the Learning Process: A Decision Tree-Based Model Using Machine Learning

2023

View full text Add to dashboard Cite

Machine learning approaches may establish a complex and non-linear relationship among input and response variables for the assessment of the Basic Education Development Index (IDEB) database and show indicators that may contribute to monitoring the quality of education. This paper uses extensive experimental databases from public schools, consisting of a case study in Brazil, to analyze data such as the physical and technological structure of schools and teacher profiles. The research proposes decision tree-based machine learning models for predictions of the best attributes to positively contribute to IDEB. It employs a newly developed SHapley Additive exPlanations (SHAP) approach to classify input variables, so to identify variables that impact the most the final model; a non-probabilistic sample was used, composed from three official databases of 450 schools, and 617 teachers. Results show that the number of computers per student, teachers’ service time, broadband internet access, investments in technology training for teachers, and computer labs in schools are the variables that have the greatest effect on IDEB. The model applied shows high prediction accuracy for test data (MSE = 0.2094 and R² = 0.8991). This article contributes to improving efficiency when monitoring parameters used to measure the quality of a teaching-learning process. Doi: 10.28991/ESJ-2022-SIED-020 Full Text: PDF

show abstract

Factors Determining the Effectiveness of Secondary Schools in Nigeria

Cited by 7 publications

References 6 publications

Statistical analysis of childhood and early adolescent externalizing behaviors in a middle low income country

Statistical analysis of childhood and early adolescent externalizing behaviors in a middle low income country

Profiling students’ environmental care attitudes taught using Sasak Tribe local wisdom-integrated model

The Role of Technology in the Learning Process: A Decision Tree-Based Model Using Machine Learning

Contact Info

Product

Resources

About