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0. Literature review on Differentiation and engagement in computer science classrooms
Computer science offers educators aiming towards differentiated teaching within the secondary schoolroom setting a distinctive series of challenges. In particular, coding may prove to be a rigorous, exacting field that calls for a demonstration of organization and precision on the part of students before they can effectively create even the simplest programs. Computer science classes will probably witness learners utterly unfamiliar with coding and fluent pupils, in addition to those who cannot even type or need other personalized academic plans (Gregory and Chapman 2012; Shah et al. 2014). Thus, how will an educator teach a particular topic in computer science to such a diversity of learners, providing additional help to certain learners and more challenging activities to others while ensuring all learners’ engagement and motivation for smooth movement together in one single class?
This discussion assumes differentiation forms the basis for attaining equitable access within the context of computer science teaching, where all learners, irrespective of their linguistic, socioeconomic, or racial background, and sex enjoy equal chances for success. This paper proposes a strategy for resolving these issues and accomplishing the above (TechSmart 2018). The resolution put forward in this paper has been devised by reviewing numerous study findings gathered over several years of teaching computer science, impacted by regular input grounded in learner outcomes and educator feedback, transforming from traditional classroom differentiation to an encouraging, novel approach that combines computer science teaching and technological innovations.
0. Depth of Knowledge Scaffolding
The vast range of coding capability displayed by learners is potentially the greatest challenge concerning computer science differentiation. Students are typically not split into different tracking clusters as this may restrict their potential or cause exclusion (Gustiani 2019); rather, a whole-class heterogeneous approach is adopted. For aiding learners with diverse skills and learning profiles to collectively get done with the same task, all tasks are accorded five scaffolding differentiation levels.
The abovementioned levels differentiate based on the Depth of Knowledge theory put forward by Norman Webb, enabling learners to learn/ practice the same topic at suitable depth levels to challenge and support them (Hess 2006). While lower scaffolding levels offer them more direction by concentrating on activities that involve calling to mind and applying concepts, higher levels slowly do away with scaffolding and concentrate on critical thinking, planning, and analysis.
Table 1: Scaffolding levels for coding activities
Depth of Knowledge (DoK) level
DoK Level 1
Learners focus on precision, undertake tasks for retrieving data, and compare their work with a model to ensure correctness
DoK Level 2
Learners fit together pieces for constructing frameworks, demonstrating concept mapping to content prepared
DoK Level 3
Learners are presented with an activity to complete, which they break down into various steps, followed by identifying the right concepts to put into use and generalizing the kinds of solutions to different issues
DoK Level 4
Presented with a big problem, learners split it up into several activities and comprehend the interrelationship between different activities
DoK Level 5
Offered a goal, learners assess and enumerate issues to be resolved, followed by activity organization in the right order, and ascertainment of the ideal strategy to resolve issues
A critical point to remember is that all levels include the previous one’s skills. In other words, a level will not replace the tasks of the previous one; rather, it will supplement the latter by eliminating scaffolding and demanding more profound understanding. For acquiring mastery over coding, consistent application of the right syntax and remembering code commands (i.e., levels 1-2) represent key skills. Even when learning revolves around critical thinking and problem resolution (i.e., levels 3-5), the above basic capabilities are practiced on an ongoing basis instead of being ignored using code-adjacent tasks abstracting or obscuring them (Lindner and Schwab 2020).
With all exercises that comprise the above levels of scaffolding, the entire class will be able to progress collectively with the aid of an ingeniously-planned lesson activity progression while, according to individual learners, the right degree of challenge and guidance. As educators are easily able to shift every learner from one level to the next for distinct exercises, learners aren’t divided into restrictive, permanent tracks; instead, they are allowed to go forward through coding capability levels at the appropriate speed to attain efficiency as well as confidence (TechSmart 2018).
0. Assisting with Syntax
The field of computer science, seemingly, has an especially steep entry barrier among academic disciplines. Owing to coding’s nature, learners are required to key in the precise symbols and words, usually using idiosyncratic syntax, for writing code interpretable by computers. Additionally, several learners may struggle with typing using the keyboard, potentially glacially slowing down coding. How is scaffolding able to ease the above challenges when helping learners acquire mastery over them?
For answering the above question, consider level 1 scaffolding of coding exercises. Learners are provided with the starter code and comprehensive comments presenting students with the program’s whole organization and structure, thereby facilitating their precision and recollection by completing individual code lines. Such comments clarify individual lines’ purpose while consistently including keywords for individual code commands to be utilized. These may be pasted/ typed on to Code Assist, which is a coding aid. Here, they will come across examples and explanations of distinct coding command usages and template codes that may be pasted into programs (i.e., they need not be typed but only tweaked for fitting their specific use within the program in question) (Kaur 2017). Using Code Assist alongside starter codes, learners can be taught concepts of computer science and recall codes without having to remember specific code syntax or key in excessive text.
Table 2: Example of Python Starter Code at Scaffolding Level 2
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