Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines

Abstract

Nowadays, regional IT industry lacks human resources because of the pressure created on the labor market by the high-value economic projects. Tutors tend to be more and more loaded with teaching, research, and administrative tasks. Students tend to use more and more electronically devices like laptops, tablets, and mobile phones in their learning sessions. In this context, universities should rely more on technologies like: LMSs (Learning Management Systems), MOOCs (Massive Open Online Courses), and why not GLOs (Generative Learning Objects) or evenAGLOs (Auto-generative Learning Objects). Auto-generative learning objects are reusable pedagogical patterns to be instantiated with generated content based on random numbers to fulfill the learning objectives. Many online e-learning resources are available containing interactive presentations, gamifications of several learning objectives. Such e-learning resources are hard to reuse and even harder to modify and adapt to; each discipline needs this because it needs access to the source code, programming knowledge to change, test and deploy etc. In this paper, we will focus on computer science disciplines needed in the regional IT industry, namely data structures and algorithms. We will show how a tutor can build several auto-generative learning objects in order to assess the knowledge of a class of students. We will start with the design of the generic models, then we will assess the generated content created with the help of a tool based on meta-programming, afterwards, we will deploy the content to a webserver to be consumed by the students. Finally, we will evaluate the assessed results and discuss the approach both from the student’s and the tutor’s perspective.