Back

Corporal Punishment in Schools in the Jaffna District of Sri Lanka - A students view

Sathiadas, M. G.; Antonyraj, A.; Viswalingam, A.; Arunthavavinajagamoorthy, S.

2020-12-15 scientific communication and education
10.1101/2020.12.15.422836 bioRxiv
Show abstract

PurposeCorporal-punishment has been prevailing in Sri- Lanka despite the strict law against it. Objectives of this study was to find the prevalence of corporal punishment in schools and the knowledge, perceptions and actions in the children. MethodsA community based cross sectional descriptive study done among school students from Jaffna district using a pretested questionnaire containing 40 questions. Multistage stratified proportionate cluster sampling was used to recruit the students. Scoring systems were used to determine the knowledge and perception of Corporal-punishment. Data was analyzed using SPSS version 20. ResultsA total of 1130 students were recruited with mean age of 17.58{+/-}0.5y. A total of 687(60.7%) said they received some form of corporal punishment at least once during the school term. Physical punishment (64.5%) was common followed by psychological (27.1%). Teachers (54.8%) were the main group involved in corporal-punishment. Mean score for the knowledge was 11.11 and a good knowledge score was seen in 37.3% of the students. Participants with good knowledge favored law against it (p value<0.05), take legal action (P value<0.05) and supported complete prohibition of it (P value<0.05). Nearly 2% (n=22) had good perception and 40.8% (n=461) had a more positive perception. Majority (86.3%) preferred an alternative way of discipline and felt it was detrimental to the future (89.5%). ConclusionCorporal-punishment was experienced by majority of the students in school and the students were aware that it was illegal and those with good knowledge were willing to take legal action.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.