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Abstract

Numerous studies have been conducted from the perspective of high school students, examining the factors that contribute to their refusal to pursue higher education institutions (HEIs). Presently, there is limited study investigating teachers’ viewpoints regarding their roles as change agents in encouraging students towards HEIs. Therefore, this study seeks to provide new insights from educators that can strengthen teacher training and development, as well as inform the creation of customised intervention strategies designed to promote a culture of academic aspiration and achievement among students. Grounded in the philosophical premise of social constructivism, the study employed a qualitative methodology, analysing a sample of ten teachers selected by purposive sampling. In-depth interviews were conducted, and the data were analysed using thematic analysis. Our findings provide a comprehensive understanding of the strategies teachers used and the challenges they faced when motivating students to pursue HEIs. We proposed a conceptual study framework built from these findings, illustrating the interaction between teachers’ duties, educational problems, and students’ aspirations for further education. Our research is significant as it advances theory development in educational psychology and motivation theory by revealing new themes and relationships that can enhance existing theoretical frameworks.

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PDF The journal welcomes original work on advancing research integrity in the form of empirical research, conceptual assessment, and critical analysis in all fields of science, including biology, chemistry, physics, medicine, law, economics, statistics, management studies, public policy, politics, sociology, history, psychology, philosophy, ethics, and information science. The journal seeks to publish research that makes a significant contribution to the literature and advances knowledge of accountability and integrity in research. With some exceptions, the journal is not interested in publishing studies that merely duplicate previous work or are limited in generalizability (e.g., due to problems with statistical design or research focus) or that in other ways fail to contribute meaningfully to the scholarly literature

The science, technology, engineering, and mathematics (STEM) education has emerged as a global education priority, many countries have printed their development strategies to national blueprints and recognized STEM education as a key role in the effective implementation of skills training in the 21st-century. Project-based learning (PjBL) and problem-based learning (PrBL) or PBL is significant in cultivating students’ knowledge and skills, integrating PBL into STEM (PBL-STEM) education is a new attempt in recent years to integrate student-centered learning methods into the STEM learning process. This study aims to explore the development trends of PBL-STEM and provide recommendations to support talent cultivation in the digital era. Based on a systematic review of 1999 articles in SCOPUS database, this study uses content analysis to identify key themes in PBL-STEM. The findings indicate that the construction of student-centered PBL curriculum, the development of innovative teaching models, and interdisciplinary integration are central to the evaluation of PBL-STEM. Moreover, the integration of emerging technologies and multi-disciplinary approaches enhances STEM competency to cultivate 21st-century skills. PBL-STEM emphasize students’ higher-order abilities, positioning it as a strategic reform for future society. This study reveals the importance of curriculum innovation, instructional reform, evaluation mechanisms, and the professionalization of STEM educators are fundamental with the rapid technological advancement.

Training the next generation of computational biologists The Department of Computational and Systems Biology offers research-centered training opportunities for learners at every stage, from high school summer research to undergraduate research experiences, master’s training and doctoral study. Our educational programs prepare students to build and apply computational methods that address real biomedical problems in genomics, structural biology, bioimage analysis, systems modeling, machine learning, drug discovery and biotechnology. Joint CMU-Pitt Computational Biology PhD Program logo Doctoral training Joint CMU-Pitt Computational Biology PhD Program Interdisciplinary doctoral training at the interface of biology, medicine, computer science, mathematics and engineering. Explore Code the Cure, Build the Future Master’s program Computational Biomedicine & Biotechnology MS Master’s training for careers in pharmaceutical R&D, biotechnology, health informatics, digital health and academia. Explore TECBio REU graphic Undergraduate research Training and Experimentation in Computational Biology REU A 10-week summer research experience for undergraduates focused on computational, quantitative and systems-level biology. Explore CompBio Academy graphic High school research CompBio Academy Summer research opportunity introducing high school students to computational biology and cancer-focused biomedical research. Explore Courses FOUNDATIONS OF COMPUTATIONAL BIOLOGY | COBB 2010 (3 , Fall) This course introduces students to the essential concepts, tools and techniques of modern computational biology with real-world examples. It covers mathematical concepts, including linear algebra, differential equations and statistics, that are central to modeling biological systems. Students will learn the basic theory behind widely used techniques, such as automated clustering, parameter estimation, sampling and numerical integration. Project-based assignments in R and Python center around real-world computational biology problems from genomics, structural biology and systems modeling. Prerequisites: Although there are no official prerequisites, it is highly recommended that students wishing to enroll in this course understand differential calculus and at least one semester of programming. This course is open to graduate students and upper-level undergraduates. INTRODUCTION TO GENOMICS (SYSTEMS BIOLOGY 1)| ISB 2020 | COBB 2021 (3 , Fall) This course introduces students to genomic data and the basic analytical principles pertaining it. Students will learn about high-throughput sequencing methods and applications, genomic variation, transcriptomics and epigenomic data. At the end of the course, the students will be able to efficiently analyze these types of datasets using existing algorithms or algorithms they will develop. INTRODUCTION TO BIOINFORMATICS PROGRAMMING IN PYTHON | COBB 2025 (3 , Fall) This course will introduce students to a selection of popular Python packages used in bioinformatics and computational and systems biology. Students will be graded on programming assignments. Each assignment will explore a different subdiscipline of computational biology and introduce students to a new Python package. Optional recitations will be available and will assist students in developing basic programming skills. MODERN METHODS IN STRUCTURE-BASED DRUG DISCOVERY | COBB 2035 (4 , Fall) This course introduces students to the modern computational approaches and governing physical and chemical principles that underpin structure-based drug discovery. The course explores how biomolecular structure and dynamics inform the rational design of therapeutics and how machine learning can be harnessed to improve drug discovery. Topics include molecular interactions, statistical mechanics and thermodynamics, molecular simulations, coarse-grained and enhanced sampling techniques, free energy calculations, protein structure prediction, protein design, molecular docking and virtual screening. Students will engage with methods for structure-based drug discovery in hands-on assignments and recitations. PROFESSIONAL DEVELOPMENT | COBB 2055 (1 , Fall and Spring) This course addresses aspects of skills essential for establishing and maintaining a career in computational biomedicine and biotechnology. Topics covered include preparation of a curriculum vitae and resume, interview skills, research ethics, mentoring and communicating with managers and team members. MACHINE LEARNING FOR BIOMEDICAL APPLICATIONS | COBB 2060 (4 , Spring) Modern high-throughput techniques generate vast quantities of data—from molecule to patient—spanning whole-genome sequencing, RNA-seq transcriptome profiling, high-throughput mass spectrometry, biochemical screening, flow cytometry, high-content screening and analyses of literature and electronic medical records. To be effective, biomedical researchers require the appropriate computational tools to correctly interpret and use this data. Machine learning, the science of finding and applying patterns in data, is an essential tool for turning data into knowledge and actionable insights and has been rising in prominence in biomedical research. This course will focus on the practical aspects of effectively applying state-of-the-art machine learning methods to biomedically relevant datasets. Topics covered include mathematical foundations, practical coding skills, classical machine learning, deep learning and generative modeling.

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