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This study examined the use of a social media platform -- WhatsApp -- by computer science students for learning computing education in the context of a Nigerian education institution. Nowadays, a large community of students in higher education institutions has embraced the WhatsApp platform for social interactions which makes it a useful tool in education. In this study, students formed three closed groups, and each group had a specific computing topic they discussed. Their discussions were in the form of posting questions, providing answers to questions, or expressing knowledge on the group topic. A questionnaire was used to collect data from the participants regarding their experiences. We conducted a descriptive analysis of the students' learning outcomes. The results show that the use of social media contributes positively to students' learning achievement, and they are motivated to acquire more knowledge about different computing topics.

Background: The birthweight of a newborn is critical to their health, development, and well‐being. Previous studies that used maternal characteristics to predict birthweight did not employ a harmonised scale to assess the risk of low birthweight (LBW). Objective: The goal of this study was to develop a new instrument that uses items on a uniform scale to assess the risk of an LBW in a pregnant woman. Methods: Item response theory was employed to evaluate a similar existing scale, and some weaknesses were identified. Results: Based on the observed weaknesses of the existing scale, a new uniform scale was developed, which is a 3‐point Likert scale consisting of seven items. Conclusion: The scale, termed birthweight questionnaire, is a valuable tool for collecting data that could assist in assessing the risk of an LBW at every stage of pregnancy. [ABSTRACT FROM AUTHOR]

In this study, the artificial neural network was deployed to develop a classification model for predicting the class of a drug-related suspect into either the drug peddler or non-drug peddler class. A dataset consisting of 262 observations on drug suspects and offenders in central Nigeria was used to train the model which uses parameters such as exhibit type, suspect's age, exhibit weight, and suspect's gender to predict the class of a suspect, with a predictive accuracy of 83%. The model sets the pace for the implementation of a full system for use at airports, seaports, police stations, and by security agents concerned with drug-related matters. The accurate classification of suspects and offenders will ensure a faster and correct reference to the sections of the drug law that correspond to a particular offence for appropriate actions such as prosecution or rehabilitation. [ABSTRACT FROM AUTHOR]