Technology-based learning environments
R. Pirouzmand; M. Rostaminezhad; N. Mohammadhasani; M. Ayati
Abstract
Background and Objectives: Recent advances in computer technology, artificial intelligence, and virtual reality technology have enabled instructional designers to create on-screen pedagogical agents in multimedia learning environments. But what is needed is research on how to make pedagogical agents ...
Read More
Background and Objectives: Recent advances in computer technology, artificial intelligence, and virtual reality technology have enabled instructional designers to create on-screen pedagogical agents in multimedia learning environments. But what is needed is research on how to make pedagogical agents more effective in improving learning at the highest possible level for learners. Therefore, what is currently important in the field of research on animated pedagogical agents is increasing the efficiency of pedagogical agents by using supporting strategies. The important point in the priority of choosing among the types of supporting strategies is to pay attention to the individual differences of learners, which determines the necessity of using different types of strategies. Among the individual differences that can affect the learning and processing of learners and need to be considered by designers in educational design is the difference in the cognitive style of learners considering the importance of individual differences in the design of educational content; also, considering the importance of visual attention in the process of receiving and processing educational content, the present research was conducted with the aim of investigating the effect of visual signaling by the animated pedagogical agent on the attention of students with field-dependent cognitive style in a multimedia learning environment.Methods: The current study was applied research and a single-subject quasi-experimental design, and A-B-A design with follow-up was used in it. First, grouped embedded figure test (GEFT) was conducted and three students with field-dependent cognitive style who had the conditions to participate in the experiment were selected. Then, in the baseline stage, the participants were exposed to English grammar training during 8 sessions using multimedia with animated pedagogical agent, and the eye tracking data of the participants were collected at the same time. Afterwards, the participants were exposed to English grammar training for 8 sessions in the test stage using multimedia with animated pedagogical agent along with visual signaling and the desired data were collected according to the previous phase. The stage of returning to the baseline was also done for 8 sessions, like the baseline stage. Descriptive and inferential statistical methods were used for data analysis using SPSS software and visual chart analysis.Findings: The findings of the research showed that, with the addition of visual signaling to the animated pedagogical agent, the attention of the learner with field-dependent cognitive style increased to the educational content (F=42.09, p=0.001). Also, the examination of the visual diagrams in the present study showed, the back and forth of the learners' attention between the content and the pedagogical agent in the intervention situation was effective and targeted.Conclusion: Considering the positive effect of accompanying visual signaling with animated pedagogical agent on the attention of learners with a field-dependent cognitive style, the design of educational multimedia together with animated pedagogical agent with visual signaling can be used as useful educational content for learners with this cognitive style. Also, considering the positive effect of simultaneous visual signaling with verbal and non-verbal cues of the pedagogical agent, on the targeting of the selection process, in learners with cognitive style dependent on the field, it is recommended to use the sum of verbal and non-verbal cues of the pedagogical agent in the design and compilation of multimedia educational contents.
e-learning
S. Hamedinasab; M. Ayati; M.َA. Rostaminejad; F. Seraji
Abstract
Background and Objectives: To use social media effectively, we need to identify and apply the implications of supportive theories using social media. Different learning theories provide a variety of interpretations of learning. Different learning theories lead to diverse orientations and outcomes in ...
Read More
Background and Objectives: To use social media effectively, we need to identify and apply the implications of supportive theories using social media. Different learning theories provide a variety of interpretations of learning. Different learning theories lead to diverse orientations and outcomes in curriculum planning. Therefore, learning theories serve as a framework for guiding decisions during the design and implementation of the curriculum. The aim of this study was to identify the types of learning theories that support the use of social networks and to analyze learning theories on social networks in order to provide a theoretical basis for designing curricula. Methods: For this study, qualitative content analysis method was used. The statistical population of this study consists of all valid documents related to the subject- supportive theories of learning in social networks- from 1995 to 2018 (n=30) selected based on purposeful sampling. The content of 30 documents were analyzed. The analysis unit is the theme. To analyze the findings, the main and sub-classes were extracted after open coding. To ensure the validity of the research, the methods of review by members, triangulation of data sources and review by colleagues were used, and to ensure reliability, the agreement method between the two coders was used. Findings: The findings showed a variety of context-based, community-based, and person-centered theories that support learning on social media. The findings also include four main categories: It showed the dimensions of social networks, network interactions, types of learning, and curriculum elements. It was shown that network communication in social networks includes social interaction, interpersonal communication and interactive communication with admin and teacher-student communication, which is broad and based on community sharing, central agreement and based on sharing ideas. Types of learning on social networks include; network learning, situational learning, problem-based learning, personal learning, and indirect learning. Elements of the curriculum include; network objectives, network content, network learning environment, network inclusiveness, and network evaluation. Network Objectives; features such as unpredictability, divergence, unpredictability, variability, nonlinearity, flexibility, value, reproductive and interactive. Network content has some features such as distributed knowledge, multiplicity of resources, reliable resources, accessibility, context-based knowledge, shared knowledge, inclusive knowledge, self-centered information, voluntary knowledge creation, interchangeable content. The network environment includes technical and educational features. Among the technical features of this environment, we can mention the diversity of language, the existence of communication tools, the possibility of customizing the message and simulating communication. The educational features of this environment also include; being rich is one of the tools of knowledge management, situational awareness, personalized information, open and flexible environment. The network learner is knowledgeable and up-to-date, independent and active, able to do several things together. The network learner is an actor, and social agent who processes, publishes, and manages knowledge on a regular basis. Evaluation on social media is a nonlinear process, formative, conscious, and continuous that is accompanied by the elimination of standard rankings and tests. Conclusion: Based on the research results, it is suggested that those in charge of education use the coordinates of social network-based curriculum elements to design a curriculum based on social networks. For further research it is suggested that researchers implement the findings in an experimental environment to take a positive step towards the optimal use of social networks to learners’ learning outcome.
