The present study has introduced a complete ICT based Mathematics Skill Development Program (MSDP) web service that aims to enhance the positive attitudes of students towards Maths. The entire system is designed and implemented in such ways that students can learn Maths with fun and practical experiences in the classroom rather than only theoretical exercises. For the last 2 years (2018-2019), we have applied MSDP in 4 distinct primary and secondary schools in Bangladesh and followed up the students ' (N = 200) attitudes towards Maths. Findings revealed that through the MSDP program, students have developed a significant positive attitude towards Maths that helps them to overcome mathematics anxiety.

COVID-19 pandemic has disrupted schooling globally. Affecting millions of students, the conventional face-to-face (F2F) education system has been replaced by e-learning overnight. As the advantages, disadvantages, and technical challenges of this abrupt transformation were already well-documented, this study aimed to intensely scrutinize university students' experiences of e-learning practices in this new normal situation. Methods: Adopting the Interpretative Phenomenological Analysis (IPA), a smaller sample of respondents (N=25) was observed to gain deeper insights using semi-structured interviews. Data were analyzed and interpreted through thematic analysis. Findings revealed that most of the students have had unfavorable experiences with e-learning. The majority of students feel that e-learning has ruined their social relationships by isolating them from their peers and instructors. A significant number of students were observed to be anxious about their future due to unstable financial status, poor learning outcomes, and unfair evaluation processes. Moreover, poor self-esteem, anxiety, and depressive symptoms have been observed among a significant portion of the students. The overall findings of this study are meant to assist stakeholders in taking the necessary steps to address the aforementioned issues and ensure an improved learning experience, particularly in an e-learning environment.

COVID-19 pandemic has dramatically transformed the global education system to a great extent. In a short period, e-learning has been adopted globally as an al-ternative teaching-learning medium. However, this sudden transition raises many concerns about e-learning acceptability. To make a clear inference, this study investigated the acceptance of e-learning (perceived usefulness, and perceived ease of use) among university-level students using the Technology Acceptance Model (TAM). The empirical analysis was performed on a sample of 694 university students in Bangladesh during the COVID-19 crisis. Findings revealed that students' overall e-learning acceptability was not adequate (32.8%), a significant number of students (46.8%) were not satisfied with e-learning experience and that majority (70.2%) of students preferred face-to-face education systems for their future study. The study also highlighted the underlying factors that negatively affect students' e-learning acceptance such as lack of technological skills, less familiarity with e-learning, lack of simplicity, low productivity, inefficiency, and so on. The overall findings of this study are intended to assist stakeholders to understand the gaps that need to be addressed immediately to increase students ’e-learning acceptability in the future.

Technology has immensely changed the world over the last decade. As a consequence, the life of the people is undergoing multiple changes that directly have positive and negative effects on health. Less physical activity and a lot of virtual involvements are pushing people into various healthrelated issues and heart disease is one of them. Currently, it has gained a great deal of attention among various life-threatening diseases. Heart disease can be detected or diagnosed by different medical tests by considering various internal factors. However, this type of approach is not only time-consuming but also expensive. Concurrently, there are very few studies conducted on heart disease prediction based on external factors. To bridge this gap, we proposed a heart disease prediction model based on the machine learning approach which enables predicting heart disease with 95% accuracy. To acquire the best result, 6 distinct machine learning classifiers (Decision Tree, Random Forest, Naive Bayes, Support Vector Machine, Quadratic Discriminant, and Logistic Regression) were used. At the same time, sklearn.ensemble.ExtraTreesClassifier has been used to extract relevant features to improve predictive accuracy and control over-fitting. Findings reveal that Support Vector Machine (SVM) outperforms the others with greater accuracy (95%).

The purpose of the current study was to develop an effective scale that can be used to assess the severity level of Mathematics Anxiety (MA) among students. Generally, measures for assessing MA adopt primitive questionnaires and unweighted rating-scale based approaches which are predominantly intended for a particular range of students. As a consequence, this type of approach is inherently static and not effective to be used widely. To bridge this gap, considering the view of 839 students, the present study has proposed a Weighted Scoring Based Mathematics Anxiety Rating Scale (WSB-MARS) which represents a more reliable, valid, generalized, and new approach to assess the severity level of mathematics anxiety in students.  Besides, the proposed scale can be implemented as a mobile application that is applicable to the research & education field.