IPSJ SIG Technical Report Vol.2017-CLE-22 No /5/13 LMS 1,a) 2,b) (LMS) ( ) LMS LMS Consideration for Detecting Variation of Learners Behavior ba

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1 LMS 1,a) 2,b) (LMS) () LMS LMS Consideration for Detecting Variation of Learners Behavior based on a Learning Log of LMS Abstract: In order to develop the system to support learners adaptively on learning context, it is necessary to detect the variation of learners as a trigger. The Learning Management System(LMS), to suport learners on the online learning, has a feature to record a learning log, which is data to record learners mnupulation and access. This learning log may enable to process learners activities as programmable data, and to support learners adaptively through detecting the variation of learners behavior. In our practice of a blended learning course for the First-Year student, time series characteristic vectors were derived, based on a learning log of LMS. And We applied pricipal component analysis (PCA) to a data set, which was constructed from time series characteristic vectors in a certain period. Time series data of each pricipal component were derived from an eigenvector of the PCA and each time series log data. Through observing the variation of the time series data, the learners variation would be detected. This research outcome would affect to the adaptive learning system. 1. LMS LMS [1], [2], [3] LMS 1 Kumamoto University 2 Uniersity of Miyazaki a) b) LMS 1

2 2. Lakhina [4] (PCA; Principal Component Analysis) Lakhina ISP IP IP PCA 3. T n T n+1 t k (T n t k < T n+1 ) (x 1 (t k ), x 2 (t k ),, x p (t k )) x 1 (t 1 ) x 2 (t 1 ) x p (t 1 ) x 1 (t 2 ) x 2 (t 2 ) x p (t 2 ) x 1 (t k ) x 2 (t k ) x p (t k ) t k y i (t k ) p y i (t k ) = h ij x j (t k ), (T n t k < T n+1 ) j=1 h ij i h ij (T n T n+1) (T n T n+1 ) (T n T n+1) p y i (t k) = h ij x j (t k), (T n t k < T n+1) j=1 [5] LMS

3 1 1 LMS LMS LMS 1 LMS 5 PC LMS LMS LMS Moodle(Ver.2.8) Moodle Moodle 9 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 8 ) ( 9 ) IP 3

4 Moodle IP IP Moodle Moodle Moodle 11 Moodle 1 CSV Moodle ( 1 ) CSV ( 2 ) UNIX ( 3 ) IP 2 LMS IP LMS IP 3 IP 1341 IP (PC1) IP PC1 2 (PC2) IP 3 PC1 PC2 PC IP LMS 4

5 情報処理学会研究報告 図 1 第 12 回授業から 1 週間の PC1 の時系列データ 図 3 第 7 回授業から 1 週間の PC2 の時系列データ 図 2 第 13 回授業から 1 週間の PC1 の時系列データ 図 4 実践した講義の中で行った Felder らの学習スタイル質問 第 10 回授業から 1 週間の PC2 の時系列データ 人の学習ログはどんな特徴をもっているか確認することと 紙の回答をもとに 因子分析を行い 3 つの因子について した そこで 因子分析の結果である因子得点を用いて 議論を進めた 今回 因子を 3 つとしたが 得られた結果 クラスタ分析を行い 複数因子を示す学習者群が 3 つまた の寄与率をみると十分な寄与率とは言えないものであると は 2 つに分類されることを確認し 各グループごとに学習 考えている それぞれの因子は Felder の学習スタイルモ ログを考察し その特徴を確認した 今回のアプローチと デルの指標を単独で含む形で現れており Felder らの指標 は別に 学習ログをもとに分類を行い その分類されたグ の有効性を確認することができた ループがどのような学習スタイルを示す傾向にあるか確認 本研究では 学習ログと学習スタイルとの関係について 調べるアプローチとして 学習スタイルごとに分類される 2017 Information Processing Society of Japan することも必要だと考えている 今回の分析に際して Moodlle の標準ログを取得して 5

6 PC2 LMS (B) [1], :,, vol. 15, no. 1, pp (2015). [2],,,,,,,, : LMS,. SC,, vol. 114, no. 277, pp (2014). [3] : LMS, CLE, vol. 2013, no. 7, pp. 1 4, (2013). [4] A. Lakhina, K. Papagiannaki, M. Crovella, C. Diot, E. D. Kolaczyk, N. Taft: Structural Analysis of Network Traffic Flows., ACM SIGMETRICS Performance Evaluation Reiview, vol. 32, no. 1, pp (2004). [5] : R, (2015). 6

7 LMS 4, 6,10 1, 2 3, 4, PC PC PC PC PC2 PC PC2 IP IP IP 10 1 PC2 PC PC2 IP IP 2 IP PC PC

8 4, 7, 7 LMS Moodlle LMS 6,, JSPS 16K

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