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

Nowadays, the IT industry is in a human resources crisis. The students tend to use their laptops and phones more often than in the last decades. The tutors are more and more loaded with teaching, research and administrative tasks. Consequently, the universities should take into account the use of technologies like LMSs (Learning Managements Systems), MOOCs (Massive Open Online Courses), GLOs (Generative Learning Objects) or AGLOs (Auto-Generative Learning Objects). This paper, written by Ciprian-Bogdan Chirila from Politehnica University of Timisoara, focuses on computer science disciplines (data structures and algorithms) and shows the way in which a tutor can build several auto-generative learning objects in order to assess the knowledge of a class of students.

The AGLO online assessment approach

Section 2 of the paper presents related works in the area of learning objects. We can find works which present a generative model for teaching computer science disciplines using Lego robots, principles for designing e-learning tools dedicated to the local automotive andustry, a similar model to the AGLO approach controlled by parameters but enhanced with dynamic learning and evaluation functionalities and so on. We discover that there are a lot of original model which can be used in the area of learning objects.

in the third section we reach the presentation of the structure of AGLOs in the context of the approach. A figure, in which we can observe the AGLO meta-model or the definition (which contains sections like name, scenario, theory, questions, etc) is given. Each line is briefly described and analyzed.

Moving forward to section 4, we learn about the specification, design and implementation of learning objects in order to be used in the automatic online assessment. The paper focuses on a set of 10 tests from the area of trees and graphs used in laboratory evaluation of the student and it shows the way in which the tests can be implemented with AGLOs. Each test is different and consists of different activities, but the paper has a brief description of every single one.

The 5th section consists of a discussion on the complexity based on the number of symbols used in the design of the AGLO test battery. There are three aspects that have to be analyzed: symbols count, symbols percentages and parameters count for the creation of structures. Consequently, three tables are given. The first one show the number of symbols used in the design of the 10 tests. The second one shows the percentages of the three categories of symbols and the third one counts the parameters.

Concluding, AGLO models has a dynamic content and this is why students benefit from them. AGLO tests are reusable and the students may test individually its understanding of algorithms. Consequently, we may say that ANGLO models are good options for both students and tutors, as the tutors can also use AGLO to structure the content and modify and adapt it at two levels. For the future, the researchers plan to mltiply the first category of variables and to introduce levels of difficulty and adaptiveness.

Read more here: https://www.edusoft.ro/brain/index.php/brain/article/viewFile/672/750

Mihaela Guţu