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Poole, Frederick J.; Clarke-Midura, Jody – Language Learning & Technology, 2023
Research involving digital games and language learning is rapidly growing. One advantage of using digital games to support language learning is the ability to collect data on students learning in real time. In this study, we use educational data mining methods to explore the relationship between in-game data and elementary students' Chinese…
Descriptors: Computer Games, Second Language Learning, Second Language Instruction, Data Analysis
Wampfler, Rafael; Emch, Andreas; Solenthaler, Barbara; Gross, Markus – International Educational Data Mining Society, 2020
Front camera data from tablets used in educational settings offer valuable clues to student behavior, attention, and affective state. Due to the camera's angle of view, the face of the student is partially occluded and skewed. This hinders the ability of experts to adequately capture the learning process and student states. In this paper, we…
Descriptors: Photography, Handheld Devices, Student Behavior, Affective Behavior
Khongput, Somruedee – LEARN Journal: Language Education and Acquisition Research Network, 2020
This study aimed to investigate EFL students' metastrategies in learning English writing. The participants were 34 undergraduate non-English major students taking a paragraph writing course at a university in southern Thailand during the semester 2/2017. Text analysis method was employed. Students' self-reflection at the end of the course was…
Descriptors: Metacognition, Second Language Learning, Second Language Instruction, English (Second Language)
Wu, Chih-Hung; Huang, Yueh-Min; Hwang, Jan-Pan – British Journal of Educational Technology, 2016
Affect can significantly influence education/learning. Thus, understanding a learner's affect throughout the learning process is crucial for understanding motivation. In conventional education/learning research, learner motivation can be known through postevent self-reported questionnaires. With the advance of affective computing technology,…
Descriptors: Computer Uses in Education, Educational Trends, Affective Behavior, Learning Motivation
D'Errico, Francesca; Paciello, Marinella; De Carolis, Bernardina; Vattanid, Alessandro; Palestra, Giuseppe; Anzivino, Giuseppe – International Journal of Emotional Education, 2018
In times of growing importance and emphasis on improving academic outcomes for young people, their academic selves/lives are increasingly becoming more central to their understanding of their own wellbeing. How they experience and perceive their academic successes or failures, can influence their perceived self-efficacy and eventual academic…
Descriptors: Well Being, Self Efficacy, Academic Achievement, Cognitive Processes
Fazeli, Seyed Hossein – Online Submission, 2011
This study aims to explore the nature of definitions and classifications of Language Learning Strategies (LLSs) in the current studies of second/foreign language learning in order to show the current problems regarding such definitions and classifications. The present study shows that there is not a universal agreeable definition and…
Descriptors: Definitions, Learning Strategies, Second Language Learning, Classification
Blanco, Maria; Guisado, Juan J. – Language Learning Journal, 2012
This article reports on a small-scale qualitative study aimed at exploring the listening process in a group of Spanish beginners in a UK higher education context. The specific aim of the study was to inform the development of materials for listening strategy awareness-raising activities. The exploration was focused on identifying (a) strategies…
Descriptors: Listening Comprehension, Listening, Learning Strategies, Qualitative Research
Schalock, H. Del – 1968
To provide effective instruction, a taxonomic framework is proposed which (1) includes all possible learner outcomes yet is understandable and manageable, (2) provides order to currently existing taxonomies of learner outcomes, and (3) facilitates instructional planning. Domains of organismic adaptation roughly corresponding to (1) the need for…
Descriptors: Affective Behavior, Classification, Cognitive Processes, Interpersonal Competence

Feezel, Jerry D. – Communication Education, 1985
Presents a taxonomy that organizes learner activities into the dimensions of mental, social, and physical involvement. Applies the taxonomy or model to speech class activities and reports validity evidence from several research studies. (PD)
Descriptors: Affective Behavior, Classification, Cognitive Processes, Learning Processes
Feezel, Jerry D. – 1983
A comparison of several major learning taxonomies produced a three dimensional taxonomy of learner involvement on the mental, social, and physical dimensions. The six hierarchical levels of the mental dimension--recording, reacting, interpreting, analyzing, evaluating and applying, and synthesizing--indicate a synthesis of cognitive, affective,…
Descriptors: Affective Behavior, Classification, Cognitive Processes, Communication (Thought Transfer)
Gray, Charles E.; Pierce, Walter D. – 1977
This paper examines and summarizes the "Pierce-Gray Classification Model for the Cognitive, Affective, and Psychomotor Domains," a model developed for the classification of educational objectives. The classification system was developed to provide a framework that teachers could use as a guide when developing specific instructional objectives for…
Descriptors: Affective Behavior, Affective Objectives, Behavior Development, Behavioral Objectives
Mayer, Colleen A. – 1974
This booklet offers practical, easy-to-read suggestions for teachers, paraprofessionals, and parents to help them understand intellectual development and intellectual disabilities. The first section highlights some of the factors involved in intellectual development and the importance of being sensitive to different stages of learning. The second…
Descriptors: Affective Behavior, Classification, Cognitive Development, Comprehension
Hite, Herbert; Rousseau, Leon – 1968
Six tasks designed to prepare teachers to develop behavioral objectives are detailed: (1) Define "behavioral objective," and list characteristics of behavioral objectives. (2) Distinguish between objectives which are behaviorally stated and those which are not. (3) Write behavioral objectives for learning activities appropriate to your…
Descriptors: Achievement Tests, Affective Behavior, Behavioral Objectives, Classification
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