EDUCATIONAL TECHNOLOGY
- Course
- MISY565 - EDUCATIONAL TECHNOLOGY
- Department
- Master of Management Information Systems - English - Master
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 0
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
- Keywords
- -
Course Description
-
EDUCATIONAL TECHNOLOGY
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Dimensions of Educational Technology |
| Week 2 | Learning Theories in the Context of Technologies |
| Week 3 | Linking Learning Objectives, Pedagogies, and Technologies |
| Week 4 | Instructional Design Models |
| Week 5 | Systems Perspective of Educational Technology |
| Week 6 | Users Perspective of Educational Technology |
| Week 7 | Learner Experiences with Educational Technology |
| Week 8 | Midterm Examinations |
| Week 9 | Social Learning Perspective of Educational Technology |
| Week 10 | Designing Learning Activities and Instructional Systems |
| Week 11 | Learning Space |
| Week 12 | Educational Project Design and Evaluation |
| Week 13 | Emerging Issues in Educational Technology |
| Week 14 | Emerging Issues in Educational Technology |
| Week 15 | Final Examinations |
Reference Books & Course Materials
- 01 Huang, R., Spector, M.J. and Yang, J. (2019). Educational Technology a Primer for the 21st Century. Springer Nature Singapore Pte Ltd.
- 02 Newby, T., Stepich, D., Lehman, J., Russell, J. And Leftwich, A. (2019). Educational Technology for Teaching and Learning. (4th Edition). Pearson.
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- Demonstrate a thorough understanding of the theories, frameworks, and models in order to assess and comprehend the state of the art in data science.
- Review the literature and apply data science theories and methodology in new research and experiments.
- Analyze datasets using supervised and unsupervised machine learning techniques.
- Design, develop and test statistics and informatics software systems for data management, analysis and problem solving.
- Conceptualize and develop efficient visuals for a range of data types and analytical tasks, and carry out independent research on a range of theoretical and applied subjects in visualization and visual analytics.
- Obtain a high level of proficiency in communication, problem solving, research or project-related activities and function effectively as a team member or a leader to accomplish a common goal in a multidisciplinary team.
- Develop and implement optimal solutions to overcome challenges associated with managing large datasets by utilizing parallel methods, cloud computing, and non-relational data storage and retrieval (NoSQL).
- Demonstrate an understanding of the interdisciplinary of data, information, and communications, as well as the ability to evaluate the leading research methods for data collection and analysis.
- Demonstrate a deep understanding of the ethical issues surrounding the use of data and apply ethical decision making in real-world data-related applications.
- Demonstrate capability of analyzing, synthesizing, and evaluating knowledge from a wide range of fields and be capable of lifelong self-directed learning.
Po-Lo Matrix
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