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Sri Wahyuni; Jensi Arista; Ulin Nuha; Julia Astutik – Journal of Biological Education Indonesia (Jurnal Pendidikan Biologi Indonesia), 2025
Problem-solving ability is a fundamental skill that students must develop in science education. However, many students continue to face challenges in mastering this competence. This study aims to develop a valid, practical, and effective science learning module to enhance students' problem-solving skills. The research adopts a Research and…
Descriptors: Junior High School Students, Science Instruction, Instructional Effectiveness, Validity
Maria Goreti Rini Kristiantari; I Gusti Ayu Lokita Purnami; Ni Ketut Desia Tristiantari; Gede Wira Bayu; Kadek Andre Karisma Dewantara; Ni Putu Utami Dewi – International Journal of Language Education, 2025
The purpose of this study is to develop learning model of design thinking learning with a Neuro-Linguistic Programming (NLP) approach to enhance autonomous learning in early writing and reading. This study conducted base on problems that exist in strategies for teaching early writing and reading in elementary schools and seek solutions that can…
Descriptors: Neurolinguistics, Teaching Methods, Beginning Writing, Beginning Reading
Swidan, Osama; Cusi, Annalisa; Robutti, Ornella; Arzarello, Ferdinando – For the Learning of Mathematics, 2023
This paper introduces a model built upon the Method of Varying Inquiry, offering a didactical approach to problem posing and solving activities that stimulates inquiry-based learning in mathematics classrooms. The model combines the inquiry-based framework with the variation theory and with specific didactical and theoretical elements (the…
Descriptors: Teaching Methods, Mathematics Instruction, Inquiry, Active Learning
Ouhao Chen; Endah Retnowati; BoBo Kai Yin Chan; Slava Kalyuga – Educational Psychology, 2023
The worked example effect has been well documented within the framework of Cognitive Load Theory (CLT), which suggests that teaching with examples would be superior to engaging in unguided problem solving, particularly for novices, as using worked examples would reduce their cognitive load, compared to solving problems, thus facilitating knowledge…
Descriptors: Cognitive Ability, Problem Solving, Transfer of Training, Retention (Psychology)
Conrad Borchers; Paulo F. Carvalho; Meng Xia; Pinyang Liu; Kenneth R. Koedinger; Vincent Aleven – Grantee Submission, 2023
In numerous studies, intelligent tutoring systems (ITSs) have proven effective in helping students learn mathematics. Prior work posits that their effectiveness derives from efficiently providing eventually-correct practice opportunities. Yet, there is little empirical evidence on how learning processes with ITSs compare to other forms of…
Descriptors: Problem Solving, Intelligent Tutoring Systems, Mathematics Education, Learning Processes
George Kalmpourtzis; Margarida Romero – Interactive Learning Environments, 2024
Taking into account the profound impact of technology on modern education, especially during the COVID-19 pandemic, increasing academic interest has focused towards the design and application of such tools on different learning contexts. A specific area of Human-Computer Interaction, called affordance theory, focuses on the perception, design and…
Descriptors: Robotics, Artificial Intelligence, Computer Software, Teaching Methods
Lisa Stark; Andreas Korbach; Roland Brünken; Babette Park – Journal of Computer Assisted Learning, 2024
Background: Both learning and problem solving are major goals of complex problem solving in engineering education. The order of knowledge construction and problem solving in learning through problem solving, however, has not been explained in current literature. Objectives: To understand their relationships, this study compared the effects of…
Descriptors: Metacognition, Multimedia Instruction, Multimedia Materials, Eye Movements
Yueh-Min Huang; Wei-Sheng Wang; Hsin-Yu Lee; Chia-Ju Lin; Ting-Ting Wu – Journal of Computer Assisted Learning, 2024
Background: Virtual reality (VR) offers significant potential for hands-on learning environments by providing immersive and visually stimulating experiences. Interacting with such environments can bring numerous benefits to learning, including enhanced engagement, knowledge construction, and higher-order thinking. However, many current VR studies…
Descriptors: Computer Simulation, Feedback (Response), Reflection, Experiential Learning
Nurit Novis-Deutsch; Etan Cohen; Hanan Alexander; Liat Rahamian; Uri Gavish; Ofir Glick; Oren Yehi-Shalom; Gad Marcus; Ayelet Mann – Journal of the Learning Sciences, 2024
Background: This paper explores K-12 interdisciplinary learning in the humanities (IL-Humanities), an area that, until now, has seen limited research focus compared to its STEM counterparts. We asked: (1) What are the outcomes of IL-Humanities in terms of interdisciplinary competences? (2) How do learners in these environments engage in…
Descriptors: Interdisciplinary Approach, Humanities, Humanities Instruction, Competence
Jiaxin Cui; Fan Yang; Yuanyi Peng; Saisai Wang; Xinlin Zhou – Infant and Child Development, 2024
Symbolic and situational mathematics are the two major representations of mathematical knowledge. Although previous literature has studied the relationship between the two from the perspective of teaching practice, learning effectiveness and behavioural performance, there is still a lack of empirical psychological research on cognitive mechanisms…
Descriptors: Mathematics Education, Symbols (Mathematics), Learning Processes, Elementary School Mathematics
Wackerly, Jay Wm. – Journal of Chemical Education, 2021
This commentary provides an overview of abduction, also known as Inference to the Best Explanation, and argues that the term and relevant problem-solving methods should be adopted by chemistry educators. Abductive reasoning, especially within the context of science and medicine, continues to be an active area of exploration for philosophers and…
Descriptors: Organic Chemistry, Science Instruction, Teaching Methods, Logical Thinking
Xie, Heping; Zhao, Tingting; Deng, Sue; Peng, Ji; Wang, Fuxing; Zhou, Zongkui – Journal of Computer Assisted Learning, 2021
Eye movement modelling examples (EMME) are computer-based videos displaying the visualized eye gaze behaviour of a domain expert person (model) while carefully executing the learning or problem-solving task. The role of EMME in promoting cognitive performance (i.e., final scores of learning outcome or problem solving) has been questioned due to…
Descriptors: Eye Movements, Attention, Cognitive Ability, Learning Processes
Talandron-Felipe, May Marie P.; Rodrigo, Ma. Mercedes T. – Research and Practice in Technology Enhanced Learning, 2021
The incubation effect (IE) is a problem-solving phenomenon composed of three phases: pre-incubation where one fails to solve a problem; incubation, a momentary break where time is spent away from the unsolved problem; and post-incubation where the unsolved problem is revisited and solved. Literature on IE was limited to experiments involving…
Descriptors: Problem Solving, Physics, Educational Games, Learning Processes
Venkat, Radhika; Gupta, Amit; Banerjee, Jayanta; Chellappan, Ramesh Babu – Action Learning: Research and Practice, 2021
Scholars have examined Revans' problem-solving praxeology in many contexts but have not fully explored the concept in the case of physical co-location. Hence, we focussed on investigating Revans' conceptualisation in a co-located context by paying particular attention to the 'different forms of learning' that emerged from it. The research setting…
Descriptors: Problem Solving, Experiential Learning, Informal Education, Foreign Countries
Xiaoli Huang; Wei Xu; Ruijia Liu – International Journal of Distance Education Technologies, 2025
This article presents a meta-analysis of the existing literature using Stata 18.0, focusing on the effects of ITSs on learning attitudes, knowledge acquisition, learner motivation, performance, problem-solving skills, test scores, and educational outcomes across different countries and educational levels (k = 30, g = 0.86). The findings suggest…
Descriptors: Intelligent Tutoring Systems, Outcomes of Education, Learning Motivation, Student Attitudes

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