教育資料探勘已經被應用於不同的教育資料集以解決在教育現場上的問題。然而，在過去的研究中，本研究發現沒有相關研究探討受測者在真實環境的專注力與不同認知負荷之間的關聯性。另一方面，目前的概念圖皆為教師手動建構，且概念圖無法隨著課程或單元的更新以自動化即時動態的生成。因此，本研究使用教育資料探勘以探討資料分析(實驗1)與技術應用(實驗2)於認知負荷之影響。在實驗1，本研究利用教育資料探勘之關聯法則探勘以探討受測者在不同的真實環境下其專注力與認知負荷之間的關聯性。在實驗2，本研究利用教育資料探勘與文字探勘以技術應用的方式開發結合動態關聯式概念圖的網路文章檢索代理人。
根據實驗1的結果，超出負荷的受測者無法在整體的實驗活動保持長時間的專注力。高負荷的受測者在實驗活動的初期就有極高的機率呈現不專注的狀態。中負荷的受測者可以在整體的實驗活動中保持高連續專注的狀態。低負荷的受測者在實驗活動的初期皆會出現平均專注值低於專注基準值的情形。在實驗2中，根據子實驗1的結果得知實驗組使用結合動態關聯式概念圖的網路文章檢索代理人的學習成效顯著高於控制組使用Google搜尋引擎，且實驗組的認知負荷顯著低於控制組。此外，學生在使用本研究所提出的系統皆有良好的科技接受程度。另一方面，根據子實驗2的結果得知學生在第二階段使用結合動態關聯式概念圖的網路文章檢索代理人的學習進步顯著高於學生在第一階段使用Google搜尋引擎。
有鑒於此，本研究利用教育資料探勘以分析專注力與不同認知負荷層級之間的關聯規則，且結合動態關聯式概念圖的網路文章檢索代理人不會造成學生過高的認知負荷。因此，未來的相關研究即可透過本研究所提出的方法有效地根據受測者的專注值變化以推估其認知負荷，也能透過開發系統以減輕學生的認知負荷。

Educational data mining has been applied to different educational datasets to solve problems in the field of education. However, in the past research, this study found that there is no relevant research to explore the correlation between attention and cognitive load of students in different real environments, the currently available non-invasive, head-mounted EEG equipment cannot directly measure a subject’s cognitive load. On the other hand, most of the concept maps are currently manually constructed by teachers and cannot be automatically and dynamically generated as a course or unit is updated. Therefore, this study used educational data mining to explore the influence of data analysis (Experiment 1) and technical application (Experiment 2) on cognitive load. In Experiment 1, association rule mining of educational data mining was used to explore the correlation between attention and cognitive load of students in different real environments. In Experiment 2, this study uses educational data mining and text mining to develop an internet articles retrieval agent combined with dynamic associative concept maps (DACMs).
According to the results of Experiment 1, the overload subjects could not sustain long-term attention in the overall experimental activities. High load subjects had a high probability of being inattentive in the early stages of the experimental activities. Medium load subjects can maintain situations of high sustained attention in the overall experimental activity. Low load subjects had a mean of attention value lower than the attention baseline value in the early stages of the experiment. In Experiment 2, according to the results of sub-experiment 1, it is known that the learning performance of the experimental group using the internet articles retrieval agent combined with DACMs was significantly higher than that of the control group using the Google search engine, and the cognitive load of the experimental group was significantly lower than that of the control group. In addition, students had high evaluations of the acceptance of technology of the system proposed in this study. On the other hand, according to the results of sub-experiment 2, the learning progress of the students using internet articles retrieval agent combined with DACMs in the second stage was significantly higher than that of the students using the Google search engine in the first stage.
In summary, association rule mining of educational data mining was used in Experiment 1 to effectively explore the association rules between attention and different cognitive load levels, and an internet articles retrieval agent combined with DACMs cannot develop an excessive cognitive load. Therefore, related research can use this method to effectively estimate cognitive load based on the changes in the attention value of the subjects, so cognitive load can be quantified indirectly and objectively, and can also reduce the cognitive load of students through the development of a system.

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