Pre-Assessment Tool for a Robotics Course

Education is built upon the gradual learning of material that becomes increasingly complex. Even though some fields may not require any existing knowledge, the consensus is that learners should have a general background before moving to a higher difficulty. This assumption determines the approach to the creation of a pre-assessment tool for a robotics course. However, before making a definitive decision, reviewing the existing literature surrounding pre-assessment and robotics is necessary. Ascertaining the best student assessment practice is essential in determining the most appropriate pre-assessment tool for K 12 students of 6-11 grades.

The first source is the article by Kyndra V. Middleton (2020), “The longer‐term impact of COVID‐19 on K–12 student learning and assessment”. As the name implies, the paper explores the influence of the COVID pandemic on education. Specifically, the author raises an important question of what constitutes the essence of evaluation in education. Middleton (2020) summarizes it as “the process of making a value judgment about the merit of the student’s academic accomplishment to aid in the improvement of future student learning” (p. 41). This definition accentuates the importance of focusing on students’ current capabilities.

In the article “Does pre-assessment work”, Guskey (2018) denotes three types of pre-assessment depending on the goals: cognitive, affective, and behavioral. Cognitive pre-assessment accentuates students’ knowledge without any involvement of their emotions or skills. Affective pre-assessment focuses on beliefs, interests, and predispositions while ignoring the actual knowledge and level of practice. Behavioral one is centered around skills as opposed to knowledge and talent. The most important takeaway is that the choice of a tool should depend upon the goal, as courses have different prerequisites.

It is, therefore, imperative to understand what are the requirements for mastering a robotics course. Although studying with highly sophisticated equipment, such as Lego robots, may seem challenging and requires extensive knowledge in the sphere of physics and programming, the research suggests that such prerequisites are not mandatory. In their study “Can educational robotics introduce young children to robotics and how can we measure it?”, Castro et al. (2018) write that children in all age ranges (7-14 years old) showed improved scores in Robotics questionnaires. This finding implies that special knowledge and skills are not necessary, thus limiting the choice to affective pre-assessment.

At the same time, another study delves into the cognitive principles behind coding in children. Marinus et al.’s (2018) article “Unravelling the cognition of coding in 3-to-6-year olds” analyzes children’s ability to solve coding tasks. Of particular note is the finding that “with appropriate scaffolding, children as young as three years of age can successfully use programming tools like Cubetto” (Marinus et al., 2018, p. 7). It should be noted that the tasks were simplified for children of this age.

However, applying the same algorithm to older students is still possible, considering that Cubetto is actually “a simplified version of the turtle LOGO programming task” (Marinus et al., 2018, p. 2). The difference is that instead of Cubetto, older students should be able to understand programming basics in order to successfully code a robot (Tsarapkina et al., 2019). The subsequent assessment should include questions regarding the aforementioned subject. If students are not able to correctly answer the questionnaires, they will be assessed based on their interests, thus following the idea of affecting pre-assessment.

A psychological evaluation may provide information about a student, which will be more useful than their questionnaire performance. The goal is to discover whether the students are eligible to study robotics. The logical solution is to ascertain whether their personal characteristics are compatible with this field. Robotics exists at an intersection of programming and physics, both of which require possessing an engineering mindset, which in this case means the innate drive to spend a significant amount of time analyzing the problem.

The questionnaire should therefore contain a number of statements, which have to be chosen by students. For instance, one sentence can describe one’s willingness to delve into technical details. Another statement is about the inclination towards finding and proceeding with the first available solution. It should be evident that these sentences describe different personalities, the latter of which is less compatible with robotics because of the amount of attention necessary to learn this field. Other questions should further delineate individuals with a predisposition toward the comprehension of complex technical information. As a result, the assessment will eliminate people prone to rush decisions and unable to process large volumes of technical data, leaving students with a suitable mindset.

Altogether, the literature review has underscored two effective methods of evaluating students – testing their knowledge and evaluating their character. The tool will take the form of two separate questionnaires, one of which will test students’ understanding of programming, while the other will analyze their personality traits. If a person already has some background in programming basics or is capable of learning complex technical information – there is a high chance that such an individual will succeed in learning the robotics course.

References

Castro, E., Cecchi, F., Valente, M., Buselli, E., Salvini, P., & Dario, P. (2018). Can educational robotics introduce young children to robotics and how can we measure it?. Journal of Computer Assisted Learning, 34(6), 970-977. Web.

Guskey, T. R. (2018). Does pre-assessment work? Educational Leadership, 75(5). 1-8.

Marinus, E., Powell, Z., Thornton, R., McArthur, G., & Crain, S. (Eds.) (2018). Proceedings of the 2018 ACM Conference on International Computing Education Research. ACM Digital Library. Web.

Middleton, K. V. (2020). The longer‐term impact of COVID‐19 on K–12 student learning and assessment. Educational Measurement: Issues and Practice, 39(3), 41-44. Web.

Tsarapkina, J. M., Petrova, M. M., Mironov, A. G., Morozova, I. M., & Shustova, O. B. (2019). Robotics as a basis for informatization of education in a children’s health camp. Amazonia Investiga, 8(20), 115-123.