Rob Koper (2005)
Increasing Learner Retention in a Simulated Learning Network Using Indirect Social Interaction
Journal of Artificial Societies and Social Simulation
vol. 8, no. 2
To cite articles published in the Journal of Artificial Societies and Social Simulation, reference the above information and include paragraph numbers if necessary
Received: 31-Oct-2004 Accepted: 21-Feb-2005 Published: 31-Mar-2005
The idea is that indirect social interaction will advise learners to select a path that has been shown to be effective. This is particularly significant when learners must choose between different alternative paths in the network, all leading to the same targets, but having different chances of success. Through indirect social interaction, the path that the learners ultimately follow is expected to converge with the path that offers them the greatest chance of success.
|Figure 1. Position and Target in an LN|
number of learners = number attained + number studying + number searching + number dropping out
The only known factor in most systems is the number of learners who have attained their targets (when there are official completion activities, like exams). Whether a learner is 'still studying a UOL', 'searching for a new UOL' or is a 'drop-out' is hard to determine. As a consequence, for this study we will concentrate on the proportion of learners who have attained their target by completing the necessary UOLs.
|Figure 2. Summary of the model|
|Figure 3. The interface of the model in the Netlogo environment|
|Settings in user interface:|
|Units of learning: 100|
|Weeks: 1040 (the number of 260 weeks, is controlled automatically; this setting only prevents early termination).|
|Vary variables in experiment:|
|matching-error: values 0 and 100|
|min-AN-quality: values 0 and 100|
|disturbance-chance: values 0 and 100|
|pheromone-strength: values 0 and 100|
|replications: values 1, 2, 3, ... , 12|
|Set up model with these commands:|
|Step model with these commands:|
|Stop after this many steps:|
|Table 1. Scaled estimates for full factorial model, factors centred by mean, scaled by range/2.|
|Figure 4. The interactions in the model|
|Table 2: Attained proportion for the interaction effects|
|Table 3: The reduced model with 6 effects|
|Analysis of Variance|
|Source||DF||Sum of Squares||Mean Square||F Ratio|
|Error||185||0.1994588||0.00108||Prob > F|
|Term||Estimate||Std Error||t Ratio||Prob > t|
|pheromone-strength × min-quality||-0.000004||9.479e-7||-4.32||<.0001|
|pheromone-strength × matching-error||0.0000063||9.479e-7||6.68||<.0001|
|Figure 5. Drop-out and attained proportions over 520 weeks, different settings|
When comparing figure 5A with 5B, we seen that the influence of pheromones on the attained proportion continues to increase after the 260 weeks selected to compare the conditions in this study. The model itself is not expected to be influenced; however, estimates of the effect on the attained proportion depend largely on the timeframe in which the pheromones work.
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