Abstract
This paper proposes a method of automatically analyzing the characteristics of e-learning contents, using e-learning time data stored in learning history databases. Although many studies exist on mathematical models of the response time, they have the disadvantage that the parameters are difficult to interpret and estimate. The response curve of e-learning time data proposed in this paper has the unique feature of deriving its two-parameter model by employing the Entropy maximization method with certain restrictions so as to make it easier to interpret the parameters. The two parameters α and β in the model are interpreted as follows: α represents the complexity of the content (i.e., the number of simple cognitive processes required to understand or solve the content) and β represents the expected time of a simple cognitive process in the content. This means that the average learning time for a content is divided into the two parameters α and β, so that the average learning time for a content is equivalent to the product of α and β. Based on these parametric properties, this paper proposes a new content evaluation method using the α-β plane, or α-β chart. Incorporating this evaluation method, the authors have developed a LMS (Learning Management System), the effectiveness of which is demonstrated in practical situations. The results show that the system let a teacher grasp contents characteristic easily and is effective for contents improvement
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Computer Systems and Applications, 2006 |
| Pages | 994-1002 |
| Number of pages | 9 |
| Volume | 2006 |
| Publication status | Published - 2006 |
| Externally published | Yes |
| Event | IEEE International Conference on Computer Systems and Applications, 2006 - Sharjah Duration: 2006 Mar 8 → 2006 Mar 8 |
Other
| Other | IEEE International Conference on Computer Systems and Applications, 2006 |
|---|---|
| City | Sharjah |
| Period | 06/3/8 → 06/3/8 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
On-line content analysis system using e-learning time data. / Ueno, Maomi; Nagaoka, Keizo.
IEEE International Conference on Computer Systems and Applications, 2006. Vol. 2006 2006. p. 994-1002 1618474.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - On-line content analysis system using e-learning time data
AU - Ueno, Maomi
AU - Nagaoka, Keizo
PY - 2006
Y1 - 2006
N2 - This paper proposes a method of automatically analyzing the characteristics of e-learning contents, using e-learning time data stored in learning history databases. Although many studies exist on mathematical models of the response time, they have the disadvantage that the parameters are difficult to interpret and estimate. The response curve of e-learning time data proposed in this paper has the unique feature of deriving its two-parameter model by employing the Entropy maximization method with certain restrictions so as to make it easier to interpret the parameters. The two parameters α and β in the model are interpreted as follows: α represents the complexity of the content (i.e., the number of simple cognitive processes required to understand or solve the content) and β represents the expected time of a simple cognitive process in the content. This means that the average learning time for a content is divided into the two parameters α and β, so that the average learning time for a content is equivalent to the product of α and β. Based on these parametric properties, this paper proposes a new content evaluation method using the α-β plane, or α-β chart. Incorporating this evaluation method, the authors have developed a LMS (Learning Management System), the effectiveness of which is demonstrated in practical situations. The results show that the system let a teacher grasp contents characteristic easily and is effective for contents improvement
AB - This paper proposes a method of automatically analyzing the characteristics of e-learning contents, using e-learning time data stored in learning history databases. Although many studies exist on mathematical models of the response time, they have the disadvantage that the parameters are difficult to interpret and estimate. The response curve of e-learning time data proposed in this paper has the unique feature of deriving its two-parameter model by employing the Entropy maximization method with certain restrictions so as to make it easier to interpret the parameters. The two parameters α and β in the model are interpreted as follows: α represents the complexity of the content (i.e., the number of simple cognitive processes required to understand or solve the content) and β represents the expected time of a simple cognitive process in the content. This means that the average learning time for a content is divided into the two parameters α and β, so that the average learning time for a content is equivalent to the product of α and β. Based on these parametric properties, this paper proposes a new content evaluation method using the α-β plane, or α-β chart. Incorporating this evaluation method, the authors have developed a LMS (Learning Management System), the effectiveness of which is demonstrated in practical situations. The results show that the system let a teacher grasp contents characteristic easily and is effective for contents improvement
UR - http://www.scopus.com/inward/record.url?scp=33750829180&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33750829180&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33750829180
SN - 1424402123
SN - 9781424402120
VL - 2006
SP - 994
EP - 1002
BT - IEEE International Conference on Computer Systems and Applications, 2006
ER -