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Direct linkJoaquín Cañero-Arias; Ángel Blanco-López; José María Oliva – International Journal of Science Education, 2024
This research integrates context-based learning and modelling. It presents a teaching-learning sequence (TLS) about the dissolution of gases in liquids using carbonated drinks as the context. The impact of the TLS is analysed in a longitudinal short-term study involving two groups of learners aged 13-14 years old (n=53). The results led us to…
Descriptors: Foreign Countries, Science Instruction, Models, Secondary School Science
Download full textTabinas, Camilo Apita – African Educational Research Journal, 2019
This study aimed to make a teaching model out of the instructional flows of the teachers teaching engineering chemistry course. Specifically, the study aimed to identify the teaching styles the teachers used in the actually delivery of the chemistry lessons. Thus, this is a descriptive study on the teaching style employed and learning styles…
Descriptors: Teaching Styles, Chemistry, Science Instruction, Engineering Education
Wang, Ting; Li, Min; Thummaphan, Phonraphee; Ruiz-Primo, Maria Araceli – International Journal of Testing, 2017
Contextualized items have been widely used in science testing. Despite common use of item contexts, how the influence of a chosen context on the reliability and validity of the score inferences remains unclear. We focused on sequential cues of contextual information, referring to the order of events or descriptions presented in item contexts. We…
Descriptors: Science Tests, Cues, Difficulty Level, Test Items
Muñoz, Karla; Noguez, Julieta; Neri, Luis; Mc Kevitt, Paul; Lunney, Tom – Educational Technology & Society, 2016
Game-based Learning (GBL) environments make instruction flexible and interactive. Positive experiences depend on personalization. Student modelling has focused on affect. Three methods are used: (1) recognizing the physiological effects of emotion, (2) reasoning about emotion from its origin and (3) an approach combining 1 and 2. These have proven…
Descriptors: Educational Games, Psychological Patterns, Models, Academic Achievement
Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction
Peer reviewedPoole, Richard L. – Journal of Chemical Education, 1989
Presents a method to teach stoichiometry where a problem is broken down into four factors and two cycles. Offers grams of known, moles of known, moles of unknown, and grams of unknown as the factors. Uses factor labeling as the second cycle. (MVL)
Descriptors: Chemical Analysis, Chemical Nomenclature, Educational Strategies, Instruction
Saari, Heikki – International Journal of Science Education, 2003
The purpose of this study was to construct and study the impact of a research-based sequence for teaching the concept of modelling to seventh-grade science students. We identified students' notions of models and the aspects of school science to be addressed regarding the model concept, which were then taken into account when we planned the…
Descriptors: Control Groups, Science Activities, Educational Research, Science Instruction
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection
