South African students continue to perform exceptionally poorly on international benchmarking science tests, such as the Trends in International Mathematics and Science Study (TIMSS). Research indicates various reasons underpinning this underperformance, such as a lack of student motivation to learn science and a dearth of teachers’ content knowledge. We have developed a mobile science game for Grade 5 educators and students, drawing on cultural-historical principles of teaching/learning, and this paper outlines our development of this game.
Amidst the COVID-19 pandemic, educational institutions underwent a significant transition from traditional face-to-face instruction to online learning, posing novel challenges for science teachers. This study sought to investigate the hurdles encountered by science teachers in the realm of online science instruction. Employing a quantitative methodology, data were gathered from 59 science teachers employed within a public school system situated in a Midwestern state. The findings unveiled several primary barriers to effective online science instruction, encompassing limited student engagement, technical difficulties, diminished face-to-face interaction, insufficient teacher training, and resource inadequacies. These impediments have adversely impacted teacher confidence levels and compromised the quality of science education delivery. Teachers articulated a pressing need for enhanced professional development opportunities, technical assistance, and improved access to resources to fortify their online teaching competencies. The study's outcomes furnish valuable insights into the multifaceted challenges confronting science teachers in the online teaching landscape, thereby informing the formulation of targeted strategies and supportive mechanisms to surmount these barriers.
Artificial Intelligence (AI) has revolutionized education by enabling personalized learning, automating administrative tasks, and enhancing instructional strategies. This paper explores the applications of AI in teaching and assessment, highlighting its potential to create dynamic, inclusive, and efficient educational environments. It emphasizes the crucial role of teachers and educators in leveraging AI tools to enhance their instructional practices and support student learning. In addition, it provides some practical examples from science education. Ethical considerations and future implications are discussed to ensure equitable and effective AI integration in education.
This paper presents research activity on computer-based mathematics learning to study the effectiveness of open-source teaching computer platforms (Canvas) in computer-assisted instruction. We designed a set of multiple-choice online quizzes as a dynamical flow-chart of possible paths to follow while solving a difficult math problem on differential operators, Fourier series of partial differential equations of mathematical physics. At each step in the quiz the student is helped to resolve the partial question and then the dynamical flow-chart directs the student to either repeat the calculations or conclusions for that local answer, or go to a certain file library with content to read, or even directed solve an extra additional mini-quiz on the side, usually related to prerequisite knowledge, and then come back at the same point in the main quiz. This type of computer algebra assisted assignment allows the conversion of traditional paper and pencil math answers from analog form into digital form, so the assessments can be easily processed and graded as multiple-choice quizzes.
This study aims to evaluate the effectiveness of the online learning platform Brilliant.org in improving the academic performance of 60 tenth-grade students from four public schools in the city of Barranquilla, Colombia. A quasi-experimental design with two groups will be used: an experimental group that will use Brilliant.org platform to learn linear algebra and matrix operations, and a control group that will learn through video explanations and other resources not related to Brilliant.org. The academic performance of the students will be measured before and after using the platform, and qualitative data will be collected through focus groups with each student group at the end of the research. Advanced statistical analysis based on numerical responses will be used, including a t-test to compare mean differences between the two groups, an analysis of variance (ANOVA) to compare mean differences between more than two groups, and a regression analysis to determine the relationship between variables. The results may demonstrate a significant improvement in the academic performance of students who use the platform compared to the control group. This study can contribute to current knowledge about the effectiveness of online learning platforms in the academic performance of secondary school students in Colombia.
