Name
Abstract | ||
|---|---|---|
The traditional E-learning often offers the online examination to assess the learning effect of a student after completion of the online learning. Basically, this traditional learning assessment mechanism is a passive and negative assessment mechanism, which cannot provide an real-time learning warning mechanism for teachers or students to find out problems as early as possible (including such learning conditions as 隆§absence of mind隆篓 resulting from poor learning stage or physical or psychological factor). The proposed research attempts to acquire the electroencephalogram to analyze the characteristic frequency band of the brainwave related to learning and formulate the learning energy index (LEI) for the learner at the time when the learner is reasoning logically via the proposed brain-wave detector based on the cognitive neuroscience. With the established LEI, the physical and psychological conditions of an online leaner can be provided instantly for teachers for assessment. Given that the learning system is integrated into the brainwave analytic sensing component, the system not only can provide learners an instant learning warming mechanism, but also help teachers and learning partners to further understand the causes of learning disorder of learners, and can also provide relevant learning members with timely care and encouragement. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/BIBE.2011.58 | BIBE |
Keywords | Field | DocType |
real-time learning warning mechanism,learning condition,traditional learning assessment mechanism,relevant learning member,online learning,established lei,online examination,poor learning stage,negative assessment mechanism,instant learning warming mechanism,brainwave characteristic frequency bands,cognition,psychology,real time,electric potential,cognitive neuroscience,electrodes,electroencephalography,indexation,neuroscience | Experiential learning,Active learning,Multi-task learning,Computer science,Auditory learning,Action learning,Synchronous learning,Artificial intelligence,Blended learning,Cooperative learning,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.41 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fu-Chien Kao | 1 | 9 | 3.47 |
| Han-Chien Hsieh | 2 | 2 | 0.76 |
| Wei-Te Li | 3 | 76 | 5.87 |