Role of In-service teacher training as a tool for the student’s performance in selected public secondary schools in Kisoro District

In-service training is very important in the life of a learner and general performance of the school. Student achievement is linked to numerous factors, but quality teachers are one of the most important components of student success. If school teachers do not have the tools they need to teach students effectively, their students will not get quality education. The major purpose of the study was to assess the role of teacher In-service training as a tool for the student’s performance in selected public schools in Kisoro district. The study applied a mixed methods research design which involved both quantitative and qualitative methods to collect and analyze data. Quantitative data were collected using questionnaire while qualitative data, in-depth interviews. Study sample included the district inspector of schools and District Education Officer and 238 teachers in Kisoro district. It also positively contributes to teacher’s performance. Importantly also, in-service teacher training according to the findings motivates teachers for better results. To teach effectively, teachers need access to ongoing teacher professional development. This professional development enables teachers to improve their own education through seminars, workshops, and classes among others. The study therefore recommends that teachers should frequently be afforded study leaves or time off to do training. During this period, the school can hire part-time teachers so that normal learning is not disrupted. It’s important to appreciate that continual professional development gives teacher’s time to learn and implement new strategies.

Developing and Comparing Data Mining algorithms that Work Best for Predicting Student’s Performance

Learning data analytics improves the learning field in higher education using educational data for extracting useful patterns and making better decision. Identifying potential at-risk students may help instructors and academic guidance to improve the students’ performance and the achievement of learning outcomes. The aim of this research study is to predict at early phases the student’s failure in a particular course using the standards-based grading. Several machines learning techniques were implemented to predict the student failure based on Support Vector Machine, Multilayer Perceptron, Naïve Bayes, and decision tree. The results on each technique shows the ability of machine learning algorithms to predict the student failure accurately after the third week and before the course dropout week. This study provides a strong knowledge for student performance in all courses. It also provides faculty members the ability to help student at-risk by focusing on them and providing necessary support to improve their performance and avoid failure.

Investigation of impact of Augmented Reality Technology on Interactive Teaching Learning Process

Augmented Reality (AR) is growing technology that superimpose 3D images onto real world. This enhances the user’s real-world experience. This potential of AR can be utilized effectively in teaching learning for engineering graphics course.There is visualization limitation for engineering students entering in first year, and this leads them to face difficulty in understanding and developing orthographic, isometric and section view of models. AR can empower the students to visualize the actual virtual object in 3D view to match their imagination with augmented object. In this regard initially a framework of AR is conceptualized for the course of engineering graphics & an AR application is developed. This paper mainly focuses on investigation & impact of AR technology on interactive teaching learning process in engineering graphics. Impact of this technology is measured by student’s performance in AR interactive test. The result shows increase in student’s performance in written test by 18.52% in engineering graphics and in mental rotation test by 28.97%.

The Influence of Family Factors on the Academic Performance of Schoolchildren and University Students: Review of Foreign Studies

The family is an informal institution that has a strong influence on the child and on his or her academic performance in particular. Family influence is more essential in childhood, because at a young age children depend a lot on their parents. The nature of relationships between children and parents as well as the role of family in general can change as children grow up. This is a literature review of foreign empirical studies leading to further development of a research program of the influence of family factors on the performance of schoolchildren and students in Russia. In most of the cases, the authors focus on the performance of one group - either schoolchildren or students. This paper considers the research question of whether family factors, which have proven their impact on schoolchildren academic performance, retain their impact on the performance of university students. The novelty of the review lies in its consideration of three ways in which the family impacts on student’s performance: the socio-economic status of the family, the social capital of the family, and the parental involvement in the educational process. The first two ways have been extensively studied in the research, while parental involvement is often considered as a significant factor in school performance. However, it is underestimated as factor in a university student’s performance. The review confirmed that family factors have a significant positive impact on academic performance for both schoolchildren and students. This influence depends on the nature of the relationship between parents and children and can change over time. The results are of practical interest for the researchers in the field of education and psychology, educational institutions as well as for parents. The empirical analysis of parental involvement influence on the academic performance of university students may be one of the possible research themes.

Adjusting 1st year students' teaching practice to the online environment

The current study aimed to collect relevant feedback on teaching practice effectiveness in relation to the tutors, students’ engagement in teaching practice, as well as self-reflection regarding necessary competences for a primary school teacher. Pedagogical activities within the teaching practice have considered: students’ online attendance to the classes held by the primary teacher, filling in an observation form regarding the lessons taught, mentorship session for analyzing the lessons along with the teachers, drafting a psycho-pedagogical record for a pupil, filling a form on reviewing the student’s performance and implication in the teaching practice. Having concluded the study’s results, we can now state that the teaching practice’s way of planning in the second school semester has proven efficient. The mentor-student / inter-student interaction has contributed to developing competences that a primary school teacher does require, through the feedback provided by the observation forms, as well as by involving students directly in the didactic activities, encouraging initiative and self-reflection. Objective analysis of results, suggestions, proposals, as well as difficulties encountered has made it possible to build a solid reference for future teaching practice – both online and in the classroom – and working towards improving it and all its partakers.

Deep Learning and Collaborative Filtering-Based Methods for Students’ Performance Prediction and Course Recommendation

At the beginning of a new semester, due to the limited understanding of the new courses, it is difficult for students to make predictive choices about the courses of the current semester. In order to help students solve this problem, this paper proposed a hybrid prediction model based on deep learning and collaborative filtering. The proposed model can automatically generate personalized suggestions about courses in the next semester to assist students in course selection. The two important tasks of this study are course recommendation and student ranking prediction. First, we use a user-based collaborative filtering model to give a list of recommended courses by calculating the similarity between users. Then, for the courses in the list, we use a hybrid prediction model to predict the student’s performance in each course, that is, ranking prediction. Finally, we will give a list of courses that the student is good at or not good at according to the predicted ranking of the courses. Our method is evaluated on students’ data from two departments of our university. Through experiments, we compared the hybrid prediction model with other nonhybrid models and confirmed the good effect of our model. By using our model, students can refer to the different recommendation lists given and choose courses that they may be interested in and good at. The proposed method can be widely applied in Internet of Things and industrial vocational learning systems.

The Effectiveness of Cooperative Learning in the Class of Inferential Statistics

The challenge in teaching statistics encompasses student motivation, mathematical anxiety, and student understanding. It needs an approach of education that encourages curiosity and leads to the engagement and comprehension of students. Cooperative learning is one of the teaching approaches that can be defined as learning with small groups of friends and implementing what they have learned in a lecture to achieve the same objective. Employing cooperative learning in the class of inferential statistics and assessing the efficacy of this approach is the aim of this study. The efficiency of the approach is determined based on the student's perception, the lecture’s observation, and the student's performance. The results showed that students more prefer to learn in a group during the course. While, based on the lecture’s observation, letting students sit in a group engages students positively during their lessons. After the implementation of cooperative learning, the student performance also exhibited improvement. Hence, it is tolerable to conclude that cooperative learning is efficient in increasing student engagement and performance.

Machine Learning Algorithm to Predict Student’s Performance: A Systematic Literature Review

One of the ultimate goals of the learning process is the success of student learning. Using data and students' achievement with machine learning to predict the success of student learning will be a crucial contribution to everyone involved in determining appropriate strategies to help students perform. The selected 11 research articles were chosen using the inclusion criteria from 2753 articles from the IEEE Access and Science Direct database that was dated within 2019-2021 and 285 articles that were research articles. This study found that the classification machine learning algorithm was most often used in predicting the success of students' learning. Four algorithms that were used most often to predict the success of students' learning are ANN, Naïve Bayes, Logistic Regression, SVM and Decision Tree. Meanwhile, the data used in these research articles predominantly classified students' success in learning into two or three categories which are pass/fail; or fail/pass/excellent.