october 2012 - TOJET the Turkish online journal of educational ...

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Diverger TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 Low SE 14 High SE 15 Low SE 2 High LE 7 11 Low LE 7 12 High LE 10 13 Low LE 5 14 High LE 2 15 Low LE 0 16 The learner characteristic based LECM is generated for each group by conducting multi-factor regression processing. But the sample quantity of group 15 and 16 is not sufficient (please refer to Table 1) for multi-factor regression processing. Hence, only 14 learner characteristic based LECMs are generated in this study, which represents the learning effort curve mode according to specific learner characteristic of each group. 14 learner characteristic based LECMs are shown in Figure 4. Figure 4. Learner characteristic based learning effort curve modes The findings are shown as following according to the analysis on 14 learner characteristic based LECMs: 1. The overall characteristic of learner characteristic based LECMs All learner characteristic based LECMs represent the consistent characteristic no matter what learning style or self-efficacy belongs to. For the high learning efficiency groups, the learning effort of all learner characteristic based LECMs tends to descend continuously and stays at negative learning effort value. In contrast, the learning effort of all learner characteristic based LECMs tends to ascend continuously and stays at positive learning effort value for the low learning efficiency groups. Such is in line with the learning effort curve studies (Hsu & Chang, 2011). 2. The influence of self-efficacy on learner characteristic based LECMs For high learning efficiency, no matter what learning style belongs to, the learning effort in high self-efficacy groups indicates more variance range and more descending trend compared with the one in low self-efficacy groups. That is, subjects with high self-efficacy tend to promote learning efficiency through descending learning Copyright © The Turkish Online Journal of Educational Technology 215
TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 effort continuously. Furthermore, for low learning efficiency groups, no matter what learning style belongs to, the learning effort in high self-efficacy groups indicates less variance range and less ascending trend compared with the one in low self-efficacy groups. And it is easier to approach convergent mode. That is, subjects with high self-efficacy tend to achieve learning effort saturation through adapting to the impact caused by ascending learning effort. Such lowers the risk of discontinuous learning caused by divergent ascending learning effort. These findings are in line with self-efficacy studies (Bandura, 1997; Jerusalem & Schwarzer, 1992; Kear, 2000; Schwarzer & Scholz, 2002) that it is easier for people with high self-efficacy to adapt themselves to learning frustration and recover themselves for continuous learning. 3. The influence of learning style on learner characteristic based LECM (1)Accommodator’s Learner Characteristic Based LECM Accommodator’s learner characteristic based LECM represents non-linear inflection. Learner characteristic based LECM tends to descend and then ascend in the ascending range. Or in the other way, learner characteristic based LECM tends to ascend and then descend in the descending range. Such is in line with the study findings on accommodator characteristics that an accommodator is a risk-taking person who is more easily influenced by the learning context and other learners and tends to approach tasks by trials and errors (Diaz & Cartnal, 1999; Kolb & Kolb, 2005b; Reid, 1995; Smith & Kolb, 1996; Wu, 1997). Therefore, accommodator’s learner characteristic based LECM represents tremble curve profile with several inflections of learning effort caused by the process of trials and errors. (2)Assimilator’s Learner Characteristic Based LECM Assimilator’s learner characteristic based LECM represents less learning effort variance range. Such is in line with the study findings on assimilator characteristics that an assimilator tends to establish institutionalization by integrating all the learning experiences and knowledge. An assimilator follows the model established in institutionalization in order to reduce trials and errors and risk-taking (Diaz & Cartnal, 1999; Kolb & Kolb, 2005b; Reid, 1995; Smith & Kolb, 1996; Wu, 1997). Therefore, an assimilator’s learning effort variance stays relative low because of institutionalization. (3)Converger’s Learner Characteristic Based LECM Converger’s learner characteristic based LECM represents convergent mode. Such is in line with the study findings on converger characteristics that a converger tends to keep problems converge gradually in order to get resolution through practical practices (Diaz & Cartnal, 1999; Kolb & Kolb, 2005b; Reid, 1995; Smith & Kolb, 1996; Wu, 1997). In the problem convergence process, the learning effort variance is getting lower gradually along with the progress of problem resolution. Therefore, converger’s learner characteristic based LECM tends to approach convergence. (4)Diverger’s Learner Characteristic Based LECM Diverger’s learner characteristic based LECM represents divergent mode. Such is in line with the study findings on diverger characteristics that a diverger tends to approach problem shooting by imagination and feeling with innovative explorartion (Diaz & Cartnal, 1999; Kolb & Kolb, 2005b; Reid, 1995; Smith & Kolb, 1996; Wu, 1997). A diverger’s learning effort keeps descending while his/her feeling is in line with successful learning progress. In contrast, a diverger’s learning effort keeps ascending while he/she experiences learning frustration. Therefore, diverger’s learner characteristic based LECM tends to approach divergence. DISCUSSIONS For the future research, first of all, the normalized learner characteristic based LECM for every learner characteristic based group will be established by enlarging the quantity and coverage areas of sampling. Once the normalized learner characteristic based LECMs are established, the learning characteristics of each normalized learner characteristic based LECM can be extracted by analyzing the mathematical characteristics of the curve profile of learning sectors which include descending sector, ascending sector, convergent sector, divergent sector and inflection points. And then develop learning progress diagnosis database based on the learning characteristics extracted from normalized learner characteristic based LECMs, which contains the learning progress for each specific sector of normalized learner characteristic based LECM. That is, a learner’s learning progress can be diagnosed by crossly comparing the numerical data converted from his/her learning effort curve with the corresponding learning characteristics in the learning progress diagnosis database at dynamic real-time based approach. As long as the specific learning characteristic is identified from the learning effort curve generated by the learner in the e-learning process, which is in line with the corresponding learning characteristic in the learner progress diagnosis database, then the learning progress of a specific learner can be anticipated since it tends to be similar to the corresponding learning progress in the learning progress diagnosis database. In the meantime, the modulized adaptive learning path and support is developed through interpreting the learning Copyright © The Turkish Online Journal of Educational Technology 216
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