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Rezaei-Zadeh, Mohammad; Darwish, Tamer K. – Learning Organization, 2016
Purpose: The purpose of this paper is to provide an integrated framework to indicate which antecedents of absorptive capacity (AC) influence its learning processes, and to propose testing of this model in future work. Design/methodology/approach Relevant literature into the antecedents of AC was critically reviewed and analysed with the objective…
Descriptors: Learning Processes, Transformative Learning, Discovery Learning, Models
Capacho, Jose – Turkish Online Journal of Distance Education, 2017
This paper aims at showing a new methodology to assess student learning in virtual spaces supported by Information and Communications Technology-ICT. The methodology is based on the Conceptual Pedagogy Theory, and is supported both on knowledge instruments (KI) and intelectual operations (IO). KI are made up of teaching materials embedded in the…
Descriptors: Student Evaluation, Computer Assisted Testing, Difficulty Level, Thinking Skills
Alonzo, Alicia C.; Elby, Andrew – Cognition and Instruction, 2019
As scientific models of student thinking, learning progressions (LPs) have been evaluated in terms of one important, but limited, criterion: fit to empirical data. We argue that LPs are not empirically adequate, largely because they rely on problematic assumptions of theory-like coherence in students' thinking. Through an empirical investigation…
Descriptors: Science Teachers, Physics, Models, Learning Processes
Lowry, Mark D. – ProQuest LLC, 2019
Bilingual language control refers to how bilinguals are able to speak exclusively in one language without the unintended language intruding. Two prominent verbal theories of bilingual language control have been proposed by researchers: the inhibitory control model (ICM) and the lexical selection mechanism model (LSM). The ICM posits that…
Descriptors: Bilingualism, Linguistic Theory, Language Processing, Computational Linguistics
Sunarto, M. J. Dewiyani; Hariadi, Bambang; Sagirani, Tri; Amelia, Tan; Lemantara, Julianto – International Journal of Instruction, 2020
The purpose of this study is to develop a Web- and Android-based learning application for high school students, named MoLearn. To meet teacher needs, a preliminary study was conducted by conducting interviews and field observations. The design and development follow the ADDIE model, with stages namely (1) analysis of needs, (2) design of…
Descriptors: Teaching Methods, Learning Processes, Computer Software, Science Teachers
Zonca, Joshua; Coricelli, Giorgio; Polonio, Luca – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2020
In our everyday life, we often need to anticipate the potential occurrence of events and their consequences. In this context, the way we represent contingencies can determine our ability to adapt to the environment. However, it is not clear how agents encode and organize available knowledge about the future to react to possible states of the…
Descriptors: Eye Movements, Individual Differences, Task Analysis, Futures (of Society)
Denby, Thomas; Schecter, Jeffrey; Arn, Sean; Dimov, Svetlin; Goldrick, Matthew – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2018
Phonotactics--constraints on the position and combination of speech sounds within syllables--are subject to statistical differences that gradiently affect speaker and listener behavior (e.g., Vitevitch & Luce, 1999). What statistical properties drive the acquisition of such constraints? Because they are naturally highly correlated, previous…
Descriptors: Phonology, Probability, Learning Processes, Syllables
Mao, Ye; Lin, Chen; Chi, Min – Journal of Educational Data Mining, 2018
Bayesian Knowledge Tracing (BKT) is a commonly used approach for student modeling, and Long Short Term Memory (LSTM) is a versatile model that can be applied to a wide range of tasks, such as language translation. In this work, we directly compared three models: BKT, its variant Intervention-BKT (IBKT), and LSTM, on two types of student modeling…
Descriptors: Prediction, Pretests Posttests, Bayesian Statistics, Short Term Memory
Austerweil, Joseph L.; Griffiths, Thomas L.; Palmer, Stephen E. – Cognitive Science, 2017
How does the visual system recognize images of a novel object after a single observation despite possible variations in the viewpoint of that object relative to the observer? One possibility is comparing the image with a prototype for invariance over a relevant transformation set (e.g., translations and dilations). However, invariance over…
Descriptors: Prior Learning, Inferences, Visual Acuity, Recognition (Psychology)
Rau, Martina Angela – International Journal of Artificial Intelligence in Education, 2017
Traditional knowledge-component models describe students' content knowledge (e.g., their ability to carry out problem-solving procedures or their ability to reason about a concept). In many STEM domains, instruction uses multiple visual representations such as graphs, figures, and diagrams. The use of visual representations implies a…
Descriptors: Knowledge Representation, Models, Competence, Learning Processes
Dwyer, Christopher P.; Hogan, Michael J.; Harney, Owen M.; Kavanagh, Caroline – Educational Technology Research and Development, 2017
Critical thinking (CT) is a metacognitive process, consisting of a number of sub-skills and dispositions that, when used appropriately, increases the chances of producing a logical conclusion to an argument or solution to a problem. Though the CT literature argues that dispositions are as important to CT as is the ability to perform CT skills, the…
Descriptors: Critical Thinking, Metacognition, Interaction, Personality Traits
Feng, Junchen – ProQuest LLC, 2017
The future of education is human expertise and artificial intelligence working in conjunction, a revolution that will change the education as we know it. The Intelligent Tutoring System is a key component of this future. A quantitative measurement of efficacies of practice to heterogeneous learners is the cornerstone of building an effective…
Descriptors: Intelligent Tutoring Systems, Learning Processes, Bayesian Statistics, Models
Phye, Gary D. – AERA Online Paper Repository, 2017
Within the context of complex cognitive processing and educational interventions, Woolfolk (2016) makes reference to problem solving acquisition, problem solving retention, and problem solving transfer. In each of the aforementioned types of problem solving activities, problem identification and problem representation (reflecting procedural…
Descriptors: Semantics, Problem Solving, Retention (Psychology), Cognitive Ability
Pal, Saurabh; Dutta Pramanik, Pijush Kanti; Choudhury, Prasenjit – International Journal of Web-Based Learning and Teaching Technologies, 2019
The popularity of smart learning has soared due to its flexibility, ubiquity, context-awareness, and adaptiveness. In particular, video-based m-learning has the biggest impact on the learning process. Its live and realistic features make learning interactive, easy, and fast. This article establishes the importance of video-based learning and…
Descriptors: Video Technology, Learning Processes, Learning Experience, Teaching Methods
Hill, Erin M.; Anderson, Laurie; Finley, Brandon; Hillyard, Cinnamon; Kochanski, Mark – Journal of STEM Education: Innovations and Research, 2019
What motivates and demotivates students in their engagement in at-home work for high-stakes assignments, such as test preparation and writing and revising papers? This paper outlines a student-centered method to identify learning strategies students actually use and obstacles students actually face compared to what is reported in the literature.…
Descriptors: Learning Processes, Undergraduate Students, Barriers, STEM Education

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