對於電腦科學領域而言，程式設計是一門極為重要的基礎技能，其涉及一些專業知識、抽象概念與問題解決策略等，是一個相當複雜的認知歷程，學生在學習的過程通常遭遇許多困難。過往的研究大都透過訪談、放聲思考（think aloud）與問卷來瞭解學生理解程式時的認知歷程，但這些方法較無法客觀量測精確的結果，本研究則嘗試透過腦波儀記錄參與者進行程式理解時腦電波各頻帶的強度，比較高、低成就兩者腦電波的差異，以更直接的生理證據探究、分析其認知。
本實驗的參與者為33位資工系所的學生，透過閱讀二題程式題目來記錄其間腦電波情況，為能清楚知曉參與者觀看何處，以便瞭解所觀看之程式碼與腦電波之關聯，本研究同時輔以眼動儀抓取參與者瞳孔位置，最後再透過問卷及訪談幫助釐清參與者的理解狀況。實驗結果發現，高成就者在較複雜的函式與程式結構上，例如遞迴函式、巢狀for迴圈等，顯著展現出較高的theta波、lower alpha、upper alpha、與beta波。theta波（頻率介於4-7Hz）和工作記憶有關，較高的theta波代表高成就者程式理解過程中較能成功將變數、陣列值等新資訊編碼進工作記憶；lower alpha（頻率介於8-9Hz）和注意力歷程有關，較高的lower alpha代表高成就者在程式理解過程中擁有較好的記憶與注意力表現；較高的upper alpha（頻率介於10-12Hz）則代表高成就者在程式理解過程中能較快從長期記憶中提取程式相關知識，進而理解出程式目的；另外高成就者在遞迴呼叫、遞迴結束條件等這些在整體程式運作中扮演重要角色的程式碼上有較強的beta波（頻率介於13-30Hz），beta波與由上而下注意力機制有關，表示高成就者能將注意力投注在Beacon（為程式碼中關鍵的程式敘述，代表著程式中特定的結構或操作）上，且以較有效率、有策略的方式理解程式。本研究亦發現對於一些較為困難的程式結構，例如for迴圈和if搭配運用、巢狀for迴圈、結構較大的遞迴程式等，低成就者會因有限的記憶力與注意力資源而影響認知表現，在程式設計教學時應提供變數提示或其他視覺化輔助工具以幫助學習者追蹤並理解程式。

Computer programming is a critical skill in computer science, which is a complex
cognitive activity involving programming knowledge, retrieval, logical thinking, and
problem solving. However, it is challenging for most novice programmers. Much existing
research have been trying to find the reasons why learners have difficulties in learning
programming, and most research studied learners’ programming by observing their
behavior by the methods of interview, thinking aloud, or the questionnaire study. But these
methods lack objective evidences about participants’ inner cognition. Instead of observing
learners’ outer behavior, this research investigated learners’ inner cognition during
program comprehension by the EEG techniques. The differences of EEG oscillations
between low and high-proformance students were discussed to find how they thought in
different ways while tracing and comprehending the programs.
In the experiment, thirty-three nudergrauate students were asked to wear the EEG
system for recording EEG activities while reading two programs. This research also
utilized the eye-tracker to record the participants’ eye movements to facilitate
understanding the EEG activities. Finally, interview and questionnaire were used to figure
out the situation of the participants’ comprehension was found from the experiment results
that high-proformance students showed significantly higher mean power of theta, lower
alpha, upper alpha, and lower beta. Theta band activity is related to the encoding and
retrieval of episodic memory, and the relational decoding of different types of information
in working memory. The high-performance students had higher theta power, which might
imply that they involved more in the transformation of current information and in the
encoding and retrieving information (e.g., the variable values) in episodic memory. The
lower alpha power (8-9Hz) is related to attentional processes. The high-performance
students had higher lower alpha power, which indicates that they had better memory and
attention performance. The high-performance students with higher upper alpha power
means they were faster in retrieving related programming knowledge while
comprehending the program. Besides, the high-performance students showed higher beta
power while reading key statements of the program. Beta power (13-30Hz) is related to
top-down attentional mechanism. It implies that the high-performance students could
focus on the Beacons (key statements of the program) and trace the program in more
iv
logical and strategical manner. This research also found the low-performance students had
worse performance on tracing programs with more complex construct, such as
combination of ‘if statement’ and ‘for loop’, ‘nested for loop’, and ‘large structure of the
recursive function’, due to their limited working memory capacity and attention resources.
Therefore, this research suggests that it is necessary to provide visualization tools to help
students grasp the program logic and process complex information to assist
comprehending programs while learning.

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