1. Overview of the course
   1. The role of cognition in education
   2. How technology relates to cognition
   3. The structure of the course
   4. Learning objectives and expectations


2. Behaviorism & Symbolic Cognition
   1. Watson`s argument against introspection
   2. Skinner`s defence of behaviorism
   3. Miller`s proposal on planning
   4. 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

1. The content of educational research: scientific method
2. Planning educational research
   1. Ethics
   2. Identifying problem, variables, hypotheses
3. 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;
4. Strategies for data collection
   1. Questionnaire
   2. Interview-structured/unstructured, individual/focus group
   3. Observation
   4. Tests - reliability, validity
5. Data analysis
   1. Distributions, statistical significance,descriptive/inferential; statistics, parametric and non-parametric data
   2. Choosing a statistical test
   3. Quantitative analysis-hypothesis testing, significance tests, ANOVA, regression
   4. Use of SPSS to conduct the above quantitative analyses.Interpretation of results from SPSS
   5. Qualitative - content analysis and grounded theory
6. How to write a research report.

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 the screencast, image editing, audio editing (audacity), video management, etc

1. Introduction to creating game lets for education
   1. Introduction to programming in AgentCubes
   2. Introduction to the project first strategy
   3. Links to computational thinking
2. Zone of Proximal Development and Flow relating to gameplay
   1. Skills vs. Challenges
   2. Project-First vs. Principles First
   3. Role of instruction and scaffolding
3. Theory behind creating engaging games
4. Learning through Microworlds, Construction SetSimulations
   1. Electronic Field Hockey example
   2. Pinball construction set, Minecraft vs. Second-life
5. Computational Thinking Patterns and relation to computational Thinking
6. Forward design
   1. Starting with gameplay and adding educational components later
   2. Evaluating forward design games
7. Backwards design
   1. Starting with representational systems and adding gameplay
   2. Evaluating backward design games
8. Creating and experimenting on representational systems in a variety of disciplines:
   1. Game theory
   2. Life Science
   3. Geology
   4. Statistics
   5. Others according to class
9. Playtesting and iterative game design
   1. Principles of running a good playtesting study
   2. Important details to notice in a playtest study
10. Combination design for game creation
    1. Using playtesting to recalibrate between educational and game engagement

1. Introduction to learning analytics, and benefits for stakeholders such as learners, teachers, department heads, course designers.
2. R language as the base for carrying out most of the practical/hands-on work in the course.
3. Relationship mining models and techniques.
4. Predictive methods
5. Text analytics basics
6. Social network analysis and methods for education.
7. Clustering mechanisms; factor analysis Data preprocessing and preparation; dimensionality control
8. Visualisation tools for education (learning curves, learnograms, heatmaps.)
9. Designing of smarter curriculum
10. Behaviour detection and model assignment Techniques such as performance factor analysis, Q-matrices, knowledge spaces.
11. Privacy and ethics concerns

1. Learning as a science
2. Contemporary theories of learning such as cognitive apprenticeship, situated learning, productive failure
3. Case-based reasoning, project-based learning and problem-based learning
4. Scaffolding and metacognition
5. Formative assessment
6. Conceptual change
7. New trends in technology-enhanced learning such as tangible, full-body and embodied interfaces, video games and virtual worlds
8. Computer-supported Collaborative learning including knowledge building, argumentation
9. Mobile and seamless learning

1. Frequency Distribution and graphing
2. Measures of central tendency
3. Sampling and probability
4. Confidence intervals and error bars
5. Hypothesis Testing
6. Chi-square distribution
7. T-tests and ANOVA
8. Power and sample size
9. Correlation and simple linear regression
10. Introduction to advanced regression concepts

1. Understand the factors that govern the interaction between human and computer for the purposes of learning a concept, content, skill, or practice
2. Introduction to HCI methods
3. Introduction to the learner-centered design approach and its usefulness for developing tools and technology for scaffolding the learning process
4. Learn basics of interaction design and usability
5. Introduction to the fundamental principles of user experience(UX) and user interaction (UI) and how they can support the learning process
6. Learn how to conduct user interviews and user evaluations, develop personas, design prototypes, and communicate ideas effectively In the lab component, students will explore and apply
7. HCI concepts in a wide range of technology-enabled learning environments. Lab work will include:
8. Requirement analysis and need identification with a focus on learning needs
9. Brainstorming ideas and building prototypes
10. Evaluating the prototype in terms of user interaction, usability, and support for learning
11. Perform basic data analysis and refinement of the prototype
12. Engage in peer-review activities

1. What is instructional systems design? Why is it required? What do instructional designers do?
2. Learning theories and how they inform instructional systems design
3. Basic processes of instructional design; Needs assessment and instructional goals
4. Learning objectives, Taxonomies of cognitive levels
5. Assessment: Diagnostic, Formative, Summative
6. Effective teaching-learning strategies
7. Technology-enhanced learning environments
8. Models of instructional design
9. Evaluation of instructional systems

1. Review key theories of learning and how they inform the design of learning environments
2. Introduction to the four dimensions of learning environments(Learner-centered, Knowledge-centered, Assessment-centered, and Community-centered) and in-depth review of each dimension
3. Learn how to evaluate learning environments
4. Learn about the design-based research methodology, its importance in education research, and how it is done.
5. Understand the meaning of “design”, “environments”, and“learning” and how to develop effective learning environments using the design-based research methodology.
6. In the lab component, students will apply the principles of the design of effective learning environments. Lab work will include:
7. Critique existing learning environments along the four dimensions (Learner-centered, Knowledge-centered, Assessment-centered, and Community-centered) with an emphasis on design decisions, trade-offs, design constraints, user constraints, and the learning goals of the environment
8. Design and implement a learning environment using the design-based research methodology
9. Brainstorm design ideas for the proposed learning environment based on the four dimensions, weigh the pros/cons of each of the proposed ideas and identify the best solution
10. Build prototype of the learning environment
11. Evaluate it to identify scope for further revision and refinement

1. Motivation behind multimodal data analysis: a literature review of current research
2. Capturing verbal and non-verbal communication during the learning process.
3. Capture, process, and analysis of data from multiple channels:
   1. Affective computing using facial emotion analysis and GSR
   2. Focus and attention analysis using eye-gaze data
   3. Cognitive load detection using pupillometry and EEG
4.  Align and interpret data from multiple channels
5. Ethics and experiment design
6. Project management

1. Introduction to Self-Regulated Learning (SRL)
2. SRL as Cognition, Affective, Metacognition, and Motivationprocesses
3. Review of six SRL frameworks
4. Help-seeking behaviour in SRL
5. Importance of personalized and adaptive feedback
6. Methods of measuring SRL
7. Analyzing data from multimodal, multichannel sensors
8. Course project to understand the challenges in analysing multichannel data, measuring SRL, developing an adaptive system for scaffolding based on learning behaviour

1. Introduction to Educational Neuroscience
   1. Making Connections: From Neuroscience to Education
2. Fundamental Concepts
   1. Basic Principles & Methods
   2. Neuromyths
3. Learning & Remembering
   1. Attention & Memory
   2. Different Ways of Learning
   3. Theories of Intelligence
4. Cognitive & Neural Development
   1. Effects of Stress & Sleep
   2. Social & Emotional Development
   3. Neuroscience, Instruction & Lifelong Learning
5. Cognitive Systems Related to Literacy
   1. Words & Reading
   2. Numerical Cognition
6. Future of Educational Neuroscience & Connections to Educational Technology