The Hidden Burden of Keywords: Cognitive Load and Language Differences in Novice Python Programming

Keyword recognition represents a fundamental skill in programming, yet little research has examined how novices develop this ability or how language background affects keyword learning. This study investigated cognitive load and keyword recognition accuracy amongst 27 novice programming students (15 English as an additional language [EAL] and 12 English as a native language [ENL]) during an intensive six-week Python course. Students completed a keyword recognition task at Weeks 1 and 6, identifying and classifying 23 Python keywords while reporting cognitive load using the Klepsch instrument. The results revealed no significant improvement in identification accuracy (Week 1: 39.80%; Week 6: 48.16%) or classification accuracy (40% at both time points) despite intensive instruction. The reported extraneous cognitive load significantly increased from Week 1 to Week 6 (p = 0.039, d = 0.99), contradicting Cognitive Load Theory predictions that schema automation reduces extraneous load with experience. EAL students reported a significantly higher intrinsic cognitive load (p = 0.030, d = 0.91) and a marginally lower keyword identification accuracy (p = 0.058, d = −0.54) than ENL students. All students (100%) who identified keywords also missed duplicate instances, indicating universal incomplete processing. These findings challenge assumptions about schema development timelines in programming education and document measurable linguistic barriers that persist even after substantial instruction, with implications for inclusive computing pedagogy.