The MOOC I enrolled in for Module 4 is titled “Reinforcement Learning” (RL) by the University of Alberta on Coursera. Coursera serves as an online course provider that collaborates with universities and diverse institutions to offer knowledge, certifications, and degree programs spanning a wide array of subjects. Coursera provides an extensive range of MOOCs that cover a variety of topics, encompassing technical domains such as computer science and data science, as well as humanities, science, and business, among others.

This MOOC spans a duration of 2-3 hours per week over 4-6 months, comprising four courses that collectively encompass seventeen modules. The structure entails the first course comprising four modules, the second course containing four modules, the third course also containing four modules, and the final course, course four, encompassing five modules. One notable advantage is the flexibility it offers, allowing students to progress at their own pace.

Course material is disseminated through videos and readings, with content ranging from 2 to 30 minutes, resembling the approach of ‘traditional’ courses. However, evaluating learners in an online course setting can pose challenges for instructors. In terms of assessment methodology for this MOOC, students are expected to complete one to two 45-minute quizzes based on the course material.

The combination of frequent quizzes and the final capstone project leads me to feel confident that the learners achieved sufficient knowledge and understanding of this MOOC. In comparison to courses I’ve taken at UVIC, this MOOC differs as it is independent learning, asynchronous and self-directed, therefore lacking interactions with peer connecting. Students who are self-driven, motivated, hold themselves accountable, and persevere are more likely to succeed in this style of learning. This MOOC covers key concepts of RL, underlying classic and modern algorithms in RL. 

There are several learning outcomes of this MOOC; firstly, is to assemble an RL system that is able to process automated decisions. Learners will also gain an understanding of the purpose of RL in the umbrella of machine learning (ML), deep learning (DL), supervised learning, and unsupervised learning. In addition, students will recognize the importance of RL algorithms such as Temporal Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradient, Dyna, amongst others. This MOOC also informs knowledge on how to formalize tasks as RL issues and how to allocate a solution. This MOOC course finishes by accumulating the learner’s knowledge into building a capstone. 

Once completing the RL Specialization, the learner will be rewarded with a certificate. The course material for this MOOC occurs fully virtually and in a structured setting; therefore, it is under the xMOOC approach category. The learners receive all information through their instructor, following a cognitive-behaviorist approach for the course pedagogy.

This assignment doubles as relating to my goals and to my academic interests. I chose the Reinforcement Learning Specialization as my MOOC since I explore all areas of technology in my degree. Recognizing how relevant RL is to the broader umbrella of ML and DL really interests me and inspires me to look further into it. Prior to this activity, I was unaware of MOOCs and how accessible they are to anyone. This activity motivates me to continue my education through MOOCs, which led me to predict this concept will become a common form of education. This activity aligns with my goals as it demonstrates a way for universal education that allows individuals the opportunity to advance their knowledge. Individuals often don’t pursue their interests due to financial challenges, time conflict, location, discrimination, etc. However, MOOCs allow for anyone and everyone that has access to the internet to enroll in various topics for free and easy registration and navigation. This is an example that aligns with my goal on how to improve educational accessibility and inclusion.

https://www.coursera.org/specializations/reinforcement-learning

https://www.ualberta.ca/admissions-programs/online-courses/reinforcement-learning/index.html

Smith, B., & Eng, M. (2013). MOOCs: A Learning Journey: Two Continuing Education Practitioners Investigate and Compare cMOOC and xMOOC Learning Models and Experiences. In Hybrid Learning and Continuing Education: 6th International Conference, ICHL 2013, Toronto, ON, Canada, August 12-14, 2013. Proceedings 6 (pp. 244-255). Springer Berlin Heidelberg.