Articles

Qualitatively, the study adopted a case research design. The population of this study comprised the last batch of Diploma in Basic Education (DBE) and the current first-year students of the new programme (Bachelor in Basic Education) of Mount Mary College of Education. Simple random and stratified sampling techniques were used to select 15 long essays from the final year, while 30 assignments were chosen from the first year. Nine (9) long essays from the English language, five (5) essays from Social Studies and one (1) essay from Mathematics. The main tools used for the study were students' assignment items and copies of long essays. Through an interpretative lens, the data were analysed into themes. The data and the analysis largely showed that students employed different kinds of modal verbs in their writing, including complete and periphrastic modal verbs. There was evidence that students have used these modal verbs in different ways that sometimes affected the intended meaning. These inappropriate choices are evident in both written texts and oral presentations. Findings showed that students employed modal verbs indiscriminately with little attention to contexts. It would be recommended that language teachers deal with the issue by giving written feedback on the use of modal verbs to students in their texts anytime they are given assignments.

Nowadays, graphs including bigraphs are mostly used in various real-world applications such as search engines and social networks. The bigraph or bipartite graph is a graph whose vertex set is split into two disjoint vertex sets such that there is no edge between the same vertex set. The bipartite graphs are colored using only two colors. This article checks if a given graph is bipartite or not and finds the color assignments of the bipartite graph using Java implementation.

Crime is a serious and widespread problem in their society, thus preventing it is essential. Assignment. A significant number of crimes are committed every day. One tool for dealing with model crime is data mining. Crimes are costly to society in many ways, and they are also a major source of frustration for its members. A major area of machine learning research is crime detection. This paper analyzes crime prediction and classification using data mining techniques on a crime dataset spanning 2006 to 2016. This approach begins with cleaning and extracting features from raw data for data preparation. Then, machine learning and deep learning models, including RNN-LSTM, ARIMA, and Linear Regression, are applied. The performance of these models is evaluated using metrics like Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The RNN-LSTM model achieved the lowest RMSE of 18.42, demonstrating superior predictive accuracy among the evaluated models. Data visualization techniques further unveiled crime patterns, offering actionable insights to prevent crime.