Developing and implementing a MOOC on JavaScript algorithms and data structures for Metropolia UAS
Golovanova, Elena (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202504176922
https://urn.fi/URN:NBN:fi:amk-202504176922
Tiivistelmä
This thesis presents the development and implementation of a MOOC (Massive Open Online Course) on JavaScript Algorithms and Data Structures for Metropolia University of Applied Sciences (UAS). The course is aimed at beginner to intermediate-level students and is designed to strengthen their understanding of essential programming concepts through a combination of theory and practice.
The course structure is modular and progressive, covering fundamental topics such as variables, functions, recursion, data types, arrays, objects, object-oriented programming (OOP), working with the DOM, fetching data from APIs and algorithmic thinking. As students progress, they deepen their understanding of core data structures like arrays and objects, and practice algorithms related to sorting and searching. Each module includes coding challenges and multiple-choice quizzes to assess students’ understanding before moving on to the next topic.
To support active learning, the course adopts a problem-solving approach, encouraging students to implement algorithms themselves using JavaScript. Quizzes and automated code validation provide immediate feedback, helping learners correct mistakes and deepen their understanding of key concepts.
In addition to course content development, this thesis discusses the pedagogical and technical challenges encountered during the design process, including balancing difficulty progression, creating a consistent quiz knowledge testing system, and ensuring accessibility through the Moodle learning platform. The project demonstrates how algorithmic thinking and data structure fundamentals can be taught effectively through a MOOC, combining conceptual understanding with hands-on practice.
Future improvements to the course may include the integration of gamification elements, peer code reviews, more advanced algorithmic challenges, and AI-powered assistance. This integration aims to foster engagement, collaborative learning, and personalized support, potentially offering real-time code suggestions and tailored feedback, thus enhancing the learning experience.
The course structure is modular and progressive, covering fundamental topics such as variables, functions, recursion, data types, arrays, objects, object-oriented programming (OOP), working with the DOM, fetching data from APIs and algorithmic thinking. As students progress, they deepen their understanding of core data structures like arrays and objects, and practice algorithms related to sorting and searching. Each module includes coding challenges and multiple-choice quizzes to assess students’ understanding before moving on to the next topic.
To support active learning, the course adopts a problem-solving approach, encouraging students to implement algorithms themselves using JavaScript. Quizzes and automated code validation provide immediate feedback, helping learners correct mistakes and deepen their understanding of key concepts.
In addition to course content development, this thesis discusses the pedagogical and technical challenges encountered during the design process, including balancing difficulty progression, creating a consistent quiz knowledge testing system, and ensuring accessibility through the Moodle learning platform. The project demonstrates how algorithmic thinking and data structure fundamentals can be taught effectively through a MOOC, combining conceptual understanding with hands-on practice.
Future improvements to the course may include the integration of gamification elements, peer code reviews, more advanced algorithmic challenges, and AI-powered assistance. This integration aims to foster engagement, collaborative learning, and personalized support, potentially offering real-time code suggestions and tailored feedback, thus enhancing the learning experience.