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

The aim of this course is to introduce various programming utilities of educational text data 

The learning outcomes of the course are: 
a) The learners will be able to implement educational text data analysis using programming language such as Python/R.
b) The learners will be able to perform data collection, data cleaning and pre-processing, and data visualization.
c) The learners will be able to perform analysis (e.g., sentiment analysis) of a given text data.
List of the topics in the course includes:
• Understanding text data, building a text data corpus, pre-processing text data corpus, feature engineering 
models, Bag of Words model, TF-IDF model
• Document similarity and clustering, identifying and extracting core content, automated text classification
• Building training and test datasets, evaluating classification models, key phrase extraction, text normalization, 
text visualization, information retrieval models.

This course will offer students an overview of the theories and practices involved in Educational Technology.

Topics include: Learning theories. Learning objectives and Bloom's taxonomy; constructivist and situated theories of learning; factors affecting and facilitating learning; learning styles. Technologies for creating new resources. Examples include video, multimedia, animations and simulations, Web 2.0/3.0. 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. 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.

  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. From problem solvers to tutors: a review of learning theories, constructivism, learning styles, bug representations
  2. Brief overview of relevant topics from ArtificialIntelligence: 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
  8. Mini project: implementing or extending an intelligent tutoring system

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 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. Discipline-based education research
  2. Conceptual understanding
  3. Problem-solving
  4. Metacognition and self-regulation
  5. Learning and transfer
  6. Disposition and the affective domain
  7. Instructional strategies and assessments
  8. Future directions and current research topics
  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

This course is intended to be a primer for students interested in education research. It is designed to provide them with the statistical knowledge that they need to understand research in this field and design their own experiments. In keeping with this goal, we will emphasize/de-emphasize topics according to the requirements of the educational technology domain requirements. We will use an open-source software package(e.g., R ) for lab assignments. The list of topics to be covered is given below.

  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


In the lab component, students will work with datasets related to education and apply the concepts discussed that week. They will work with software such as R or equivalent such as PSPP or SPSS.


  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

In this course, students will learn about multi-channel data and the use of multimodal analysis to understand the teaching-learning process. The course content will include:

  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. Types of Educational Apps: Courseware, Classroom aids, Assessment, Special purpose, and so on.
  2. Study of some popular Educational Apps: Put one example app of each category above.
  3. Technologies for the development of Educational Apps: Android platform, integration of videos, simulations, toolkits for Augmented Reality and Virtual Reality.
  4. Integration with other Apps: cloud services, social media,location-aware.
  5. Deployment specific considerations: K-12, Higher education, industry training, ubiquitous learning, seamless learning

In this course, students learn the theories of self-regulated learning in technology-enhanced learning environments such as intelligent tutoring systems. The course contents include:

  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

Approximately 8 weeks internship in an organization that does educational technology work, or has projects in educational technology. The objective is to familiarize the student with the interdisciplinary nature of ET work.

  1. The student will be required to work on a project in the organisation, relevant to ET.
  2. The student will make observations on how the industry carries out ET projects, from needs analysis to developing content/ interventions, to deployment and evaluation.
  3. The student will be required to identify areas where known best practices in ET can be incorporated.
  4. The student will prepare a report and make a presentation upon completion of the fieldwork. This will include their specific project, observations about the industry, and identified best-practices