|Sr.No||Course Code||Course Name||Course Description||Course Credits|
|1||ET 801||Introduction to Educational Technology||This course will offer students an overview of the theories and practices involved in Educational Technology. Topics include: 1. Learning theories. Learning objectives and Blooms taxonomy; constructivist and situated theories of learning; factors affecting and facilitating learning; learning styles. 2. Technologies for creating new resources. Examples include video, multimedia, animations and simulations, Web 2.0/3.0. 3. Instructional Design (ID). Basic ID models (eg ADDIE model), ID models for e-learning and blended learning (eg Dick and Carey model), online course development using ID. 4. Technologies for content delivery. Examples include Learning Management Systems (e.g. Moodle) classroom management systems (e.g. Jhoomla), Open Education Resources, intelligent tutoring systems. Throughout the course, we will illustrate theories with practical examples such as NPTEL, OCW, OSCAR, PhET.||6.0|
|2||ET 802||Research Project||-||6.0|
|3||ET 803||Advanced Topics in Cognition||
I. Overview of the course a. The role of cognition in education b. How technology relates to cognition c. The structure of the course d. Learning objectives and expectations
II. Behaviorism & Symbolic Cognition a. Watson`s argument against introspection b. Skinner`s defence of behaviorism c. Miller`s proposal on planning d. Newell and Simon`s Physical Symbol Systems
III. Neural networks and Connectionism
IV. Distributed Cognition (DC) a. The argument for DC b. External Representations c. Scientific discovery games and DC V. Experimental evidence for DC
VI. Dynamic systems theory a. The argument for a dynamic system approach b. Experimental evidence
VII. Ecological psychology a. The theoretical position b. Experimental evidence
VIII. Situated Robotics a. Rodney Brooks` Intelligence without representation view b. Valentino Braitenberg`s vehicles c. The origin of proto-representations d. The argument aginst Brooks
IX. Situated Cognition a. Jean Lave`s position b. Kirsh`s critique c. Experimental evidence
X. Embodied Cognition a. The theoretical framework b. Experimental evidence
|4||ET 804||Reserach Methods in Educational Technology||I. The content of educational research: scientific method II. Planning educational research; i. Ethics; ii. Identifying problem, variables, hypotheses; III. Styles of educational research: Naturalistic and ; ethnographic methods; Survey, longitudinal, cross-sectional analysis; Case-Study; Experimental, Quasi-experimental, single- case research; Factorial design; Action research; IV. Strategies for data collection; i. Questionnaire ; ii.Interview-structured/unstructured, individual/focus group; iii. Observation; iv. Tests - reliability, validity; V. Data analysis; i. Distributions, statistical significance, descriptive/inferential; statistics, parametric and non-parametric data; ii.Choosing a statistical test; iii. Quantitative analysis-hypothesis testing, significance tests, ANOVA, regression; iv. Use of SPSS to conduct the above quantitative analyses. Interpretation of results from SPSS.; v. Qualitative - content analysis and grounded theory; VI. How to write a research report.||6.0|
|5||ET 805||Adaptive Tutoring Systems||1. From problem solvers to tutors: review of learning theories, constructivism, learning styles, bug representations; 2. Brief overview of relevant topics from Artificial Intelligence: knowledge representation, reasoning, machine learning, natural language processing; 3. Building pedagogy models: intervention strategies, problem generators, dialogue models; 4. Building student models: representation for various types of problems, dynamic updating, as input to pedagogy model, behaviour modelling; 5. Building domain models: concept maps, misconceptions, dependences, processes and procedures ; 6. Case studies of adaptive and intelligent tutoring systems; 7. Building adaptive tutors: model tracing tutors, frameworks such as CTAT, distributed tutors. ; Mini project: implementing or extending an intelligent tutoring system;||6.0|
|6||ET 806||Educational Technology -Tools Lab||The course introduces the participants to a range of commonly useful tools relevant to educational technology. ET-Tools Lab course will help students in understanding tools useful for creating online learning content, online assessment, using visualization, analyzing data, etc. The tools selected in this course are practically useful to address various teaching-learning functions. Broadly the following categories are to be covered. The course format will consist of a lecture demo of select tools, and the students performing an assignment using the same. 1.Introduction to various types of Education Technology tools. 2.Content Authoring Tools: Raptivity, Articulate 3.Assessment Tools: Hot Potato, 4.Concept Mapping Tools: e.g. CMAP, MindMap, Compendium 5.Visualization Tools: e.g. R, Highcharts 6.Analytics Tools: e.g. SPSS, R-language, CAQDAS 7.Learning Management System: e.g. Moodle, Sakai 8.Educational Data Mining: e.g. Weka, Rapidminer, KNIME 9.MOOC: e.g. EdX 10.Collaboration Tools: e.g. Wiki 11.Tutoring system development. e.g. CTAT, ASPIRE 12.Animation tools. E.g. Flash, Gimp 13.Others: Camstudio for screencast, image editing, audio editing (audacity), video management, etc||4.0|
|7||ET 807||Educational Game Design||I.Introduction to creating gamelets for education a.Introduction to programming in AgentCubes b.Introduction to the project first strategy c.Links to computational thinking II.Zone of Proximal Development and Flow relating to gameplay a.Skills vs. Challenges b.Project-First vs. Principles Fist c.Role of instruction and scaffolding III.Theory behind creating engaging games IV.Learning through Microworlds, Construction Set Simulations a.Electronic Field Hockey example b.Pinball construction set, Minecraft vs. Second-life V.Computational Thinking Patterns and relation to Computational Thinking VI.Forward design a.Starting with gameplay and adding educational components later b.Evaluating forward design games VII.Backwards design a.Starting with representational systems and adding gameplay b.Evaluating backwards design games VIII.Creating and experimenting on representational systems in a variety of disciplines: a.Game theory b.Life Science c.Geology d.Statistics e.Others according to class profile IX.Play testing and iterative game design a.Principles of running a good playtesting study b.Important details to notice in a playtest study X.Combination design for game creation a.Using playtesting to recalibrate between educational and game engagement||6.0||8||ET 809||Discipline-based Education Research||Discipline based education research
Metacognition and self-regulation
Learning and transfer
Disposition and the affective domain
Instructional strategies and assessments
Future directions and current research topics
|6.0||9||ET 810||Learning Analytics and Educational Data Mining||Introduction to learning analytics, and benefits for stakeholders such as learners, teachers, department heads, course designers.
R language as the base for carrying out most of the practical/hands-on work in the course.
Relationship mining models and techniques.
Text analytics basics
Social network analysis and methods for education.
Clustering mechanisms; factor analysis Data preprocessing and preparation; dimensionality control
Visualisation tools for education (learning curves, learnograms, heapmaps.)
Designing of smarter curriculum
Behaviour detection and model assignment Techniques such as performance factor analysis, Q-matrices, knowledge spaces.
Privacy and ethics concerns
|6.0||10||ET 811||Learning Sciences||Learning as a science
Contemporary theories of learning such as cognitive apprenticeship, situated learning, productive failure
Case based reasoning, project based learning and problem based learning
Scaffolding and metacognition
New trends in technology-enhanced learning such as tangible, full body and embodied interfaces, video games and virtual worlds
Computer supported Collaborative learning including knowledge building, argumentation
Mobile and seamless learning