Introduction
- Different recommendation methods on prerequisite are proposed. Both prerequisite and subsequent learning object recommendations are performed. Recommendation of learning objects that were learned before are also covered to support review learning.
- Learning path locating helps for adaptive recommendation according to learners’ performance on the path. The recommendation methods aim at the located qualified and unqualified learning objects.
- The time decay because of forgetting is combined for learning score modification. It punishes the learning scores to indicate the real knowledge maintenance at different time points. Modified learning scores are combined into prerequisite correlation calculation and are adopted as one of the features for recommendation.
- Experiments on real-world data show the improvement of LMR both on recommendation accuracy and learning performance.
Related work
Symbols
Symbols | Description |
---|---|
\( {\text{se}}_{si} \) | Score of learner s on learning object \( i \) |
\( \overline{\text{se}}_{i} \) | Average score of learner l |
\( q\left( {i,d_{1} } \right) \) | Prerequisite correlation coefficient between learning objects i and d1 |
d1 | First unqualified learning object |
d2 | Last qualified learning object |
\( {\text{dis}}_{{si, sd_{1} }} \) | Time distance between 2 learning behaviors of learner s on learning objects i and d1. |
Ipr | Learning object set for prerequisite recommendation |
Ifr | Learning object set for subsequent recommendation |
simsr | Similarity between learner s and r |
pri | Recommendation value of learning object i for learner r |
Locating-based MOOC recommendation for prerequisite and subsequent learning objects
Learning path locating
Decay of learning score for forgetting with time on
Days | Knowledge maintenance |
---|---|
0 | 1 |
0.33 | 0.582 |
1 | 0.442 |
8 | 0.358 |
24 | 0.337 |
48 | 0.278 |
144 | 0.254 |
720 | 0.211 |
Prerequisite correlation measuring on learning scores
Qualified similar learner for collaborative filtering
- Qualified similar learners Qualified similar learners on the located objects have successful learning experience. Their learning paths are referable. Learning objects on their paths are adopted as candidates for recommendation.
- Learning object candidates according to the sequence of learning paths Usually collaborative filtering adopts all objects of similar learners as candidates for recommendation. LMR considers on the prerequisite relationship. Candidates are selected according to the sequence of similar learners’ paths. The sequence on learning paths reflects the prerequisite relation between objects. Even if the candidates may be learned by the target learner before, it is necessary to review them to solve the prerequisite inadequacy. Traditional collaborative filtering recommendation mainly covers only objects that have not been learned by the target learners.
- Time featured recommendation value Learning scores are modified with time forgetting decay consideration. The modified scores are adopted for prerequisite correlation coefficient calculation and recommendation value calculation. It helps to increase the accuracy of recommendation.
Prerequisite recommendation for the unqualified learning object
Subsequent recommendation for the qualified learning objects
Experiments
Experiment on accuracy improvement
Experiment on application to different types of learning objects
Learning objects | Arts (%) | Science (%) |
---|---|---|
Hit | 62.1 | 37.9 |
All | 48.6 | 51.4 |