Advancing Higher Ed
Flipped Classroom and Supplemental Instruction in High Structure Course Design and Their Effects on Students’ Learning in Gateway Chemistry Course
Paper Presentation in a Themed Session Lyudmyla Stackpool, Galkande Iresha Premarathna, Laura Jacobi
Redesigning high-enrollment, low division courses or gateway courses, in favor of evidence-based teaching practices improves student performance in higher education fields of science, technology, engineering and mathematics (STEM). Course redesign in favor of a blended high-structure course format based on flipped classroom and Supplemental Instruction and its effects on student performance in gateway General Chemistry course was evaluated using the logistic regression analysis. Three versions of General Chemistry II (Chemistry 202) course taught by the same instructor, were assessed based on students‘ final grades. The sample design included students enrolled in the blended predominantly online low-structure course, control section (fall 2021), with initial population of 67; two blended high-structure courses, experiment in current research: 50% online 50% face-to-face (fall 2022 and fall 2023) with initial populations 63 and 53 students respectfully. It was shown that a blended high-structure course design with the flipped classroom teaching/learning model offered in conjunction with Supplemental Instruction significantly enhanced performance of the students and led to improved student performance in the subsequent course in General Chemistry sequence, highlighting positive long-term effect of the flipped classroom learning on student performance. This research advocates for the adoption of high-structure learning environments and robust Supplemental Instruction programs as effective strategies to foster educational achievement in challenging gateway courses.
The Impact of Culture on Reflective Practice in the International Classroom : A Case Study Exploring the Experiences of Nigerian Students Studying Reflective Practice at a UK University
Paper Presentation in a Themed Session Catherine Glaister
Whilst acknowledged as an important intercultural competence, there is little academic literature exploring the impact of national culture on student engagement with reflective practice. Exploring the experiences of Postgraduate Nigerian students, this study aims to understand the impact of Nigerian culture on students’ experiences of reflective learning, thus contributing to theory and literature, as well as identifying enhanced approaches to teaching and learning. The study was undertaken jointly by two academic colleagues, one a cultural outsider from the UK who is teaching on the programme, and one a cultural insider of Nigerian origin. Fifteen, in-depth semi-structured interviews were conducted, and thematic analysis undertaken, with themes emerging around the novelty of formal reflective practice; impact; self awareness and identity; gender; religion; ethnicity; power; class and collectivism. Results show that engagement with formal reflection on action was a new concept for students, which as a primarily individualistic process, presented real challenges to students embedded in collectivist cultures. Issues around power and hierarchy emerged as barriers to criticality and engagement in reflection, along with impacts associated with gender and religion. Whilst students reported initial struggles, as they progressed the impact on their learning and their lives was felt to be significant, with some students reporting transformative learning and deep personal growth, moving beyond superficial levels of learning often reported by home students. This holds implications for both teaching and learning strategies for use in international contexts, as well as highlighting potential for integrating reflective practice into the Nigerian system.
Predicting Students’ Academic Performance Using Machine Learning Algorithms
Paper Presentation in a Themed Session Cansu Cigdem Ekin
Recently, education sectors have the most attraction from people all around the world and this make it more valuable for those wanting to invest in this sector and earn income. Students are the largest stakeholders in this area and they need more attention from the educational side. All universities are trying to improve quality for achieving their students’ satisfaction. Sometimes universities are using their old data related to students to analyze and make better decision for their future aspects. These analyses can be done by EDM (Educational Data Mining) a subset of ML (Machine Learning) that can discover very large datasets for producing valuable results. In this study we analyze the data of Computer Engineering students to predict their academic performances in three different aspects: predict final grades, study duration, and next term course grade. For this purpose, we have examined different ML algorithms and different available features to find the best ML algorithm and the most significant factors for predicting students’ academic performance. Our results show that SVM (Support Vector Machine) and DT (Decision Tree) are the two best ML algorithms and also, we have been determined that only course grades are the most valuable factors in prediction.
