Year 1

Coursework

Semester 1

Semester 2

ET 601 - Introduction to EdTech

In this Educational Technology course, I explored foundational concepts and practical tools to improve learning experiences. I began by understanding what Educational Technology is and the course’s overall scope. I studied various ET tools, weighing their pros and cons. Key learning theories like Cognitivism, Behaviorism, Constructivism, and Dual Coding Theory helped me connect theory to practice. I focused on active learning, interaction, and applying digital versions of Bloom’s Taxonomy. The course also covered effective technology integration, visualization, educational software, and Intelligent Tutoring Systems. As a project, I created ThoughtBot AI, a chatbot designed to support collaborative learning in civics. 

ET 606 - EdTech Tools Lab

The Educational Technology Tools Lab introduced me to a variety of practical tools essential for educational technology applications. The course helped me understand tools for creating online learning content, conducting assessments, visualizing data, and analyzing information effectively. It covered a broad range of categories through lecture demonstrations and hands-on assignments. I worked with tools like 3D printers for physical modeling, educational software like Betty’s Brain, AI-powered content creation tools such as Adobe Firefly and Creatie-AI, data analysis tools like Jamovi and Orange, as well as platforms for collaboration like Miro and Figma. This lab enhanced my skills in integrating technology across teaching and learning functions.

ET 610 - Learning Analytics and Educational Data Mining

The Learning Analytics and Educational Data Mining course helped me learn core concepts of LA and their benefits for learners, teachers, course designers, and administrators. The course introduced practical skills using Tableau for interactive data visualization and Python coding in Google Colab for hands-on data analysis. I explored Exploratory Data Analysis (EDA), relationship mining models, predictive analytics, text analytics, clustering, factor analysis, data preprocessing, sequence process mining, and differential sequence mining (DSM). Behavior detection and smarter curriculum design using educational data were also covered. Privacy and ethical concerns were emphasized throughout to ensure responsible use of learning analytics.

ET 612 - Statistical Methods for Educational Research

In Statistical Methods for Educational Research, I gained foundational knowledge in statistics tailored for educational research. The course covered essential topics including frequency distributions, measures of central tendency, sampling, probability, confidence intervals, hypothesis testing, chi-square tests, t-tests, ANOVA, correlation, and regression analysis. Advanced regression concepts were also introduced. Practical lab sessions involved applying these concepts to educational datasets using open-source software like R or Jamovi. This course equipped me with the statistical tools necessary to understand educational research literature and design rigorous experiments within the educational technology domain. 

ET 613