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The teaching of practical works is fundamental in scientific and technical learning disciplines, in in-class as well as in distance, and meets the learners’ needs. Unfortunately, learners are often deprived of this essential instructional teaching opportunity. This is, in fact, due to several problems. The researchers cite, among others, the unavailability of the assistants, the obstruction of learners, and the material is expensive and cannot be duplicated. To minimize these problems thus teaching must answer to these needs (Guillaume, 2006).
Due to the previously mentioned problems, the practical work in general and especially the practical work of programming languages in computer science in the introductory courses in the first university cycle in the university years is usually accomplished by a group of learners as a result of the lack of adequate devices. And therefore when the assessment is given, it is one mark for all the group members and this makes the assessment subjective and does not reflect the true level of each learner belonging to the group, because there are elements of the group that do not work and rely on the active elements. In addition to this problem, the authors cite the problem of failure to learn these types of activities as studies have proven. Knowing that the studies confirm that the rate of failure or abandonment of the programming in the introductory courses in the first university cycle varies from 25 to 80% of the share in the world (Aiouni et al., 2018). This problem of programming failures does not only concern our institution. Several studies on algorithmic / programming learning conducted by different institutions in other countries have converged towards the same conclusion (Boussaha et al.,2015a). Learning algorithmic / programming was always a source of difficulty not only for students but also for teachers too. This is due to the intrinsic characteristics of the discipline and the classic methods of teaching (classic practical works in the classes rooms).
To overcome these problems, several problem-based learning systems are developed (Tadjer et al., 2018) these systems did solve the hardware problem, but the assessment problem was still not resolved.
We think that the self-assessment, in its formative function, is in the middle of the training considering its regulating function, which is paramount. The construct of self-assessment refers to the degree to which students can regulate aspects of their thinking, motivation, and behavior during learning (Tadjer et al.,2018)(Tadjer, et al.2020). When the authors consider the topic of the self-assessment, some key generic factors should inform, the self-assessment has several different purposes for all those involved in the process these include(Hadadi & Bouaarab-Dahmani,2019):
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Measurement of learning and achievement ( Smith et al.,2013)
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Institutional promotion and marketing
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Diagnosis of learning (Sadler,1989)
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Feedback and feedforward for learners ( Nicoland and Macfarlane,2006)
Feedback for teachers (Nicoland and Macfarlane,2006)
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Certification of learning
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Development of learning outcomes for a course and program( Biggs and Tang,2011)
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Development of knowledge, skills, and dispositions for the long term, including judgment ( Boud and Falchikov,2007)
The present work concerns more particularly, the learners’ self-assessment in the CEHL (Computing Environment for Human Learning) environments of remote practical works in programming. Our goal is to suggest a self-assessment CEHL environment for thinking about measures of cognitive knowledge, a self-assessment CEHL that will help generate feedbacks, guide future research, and develop learners' efforts.