By Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Olga C. Santos
As a space, know-how more suitable studying (TEL) goals to layout, advance and attempt socio-technical ideas that might help and improve studying practices of people and corporations. info retrieval is a pivotal job in TEL and the deployment of recommender platforms has attracted elevated curiosity up to now years.
Recommendation tools, recommendations and structures open a fascinating new method of facilitate and help studying and instructing. The objective is to strengthen, installation and review structures that supply newbies and academics with significant assistance as a way to aid establish appropriate studying assets from a probably overwhelming number of choices.
Contributions handle the next subject matters: i) person and merchandise facts that may be used to help studying advice structures and situations, ii) cutting edge tools and strategies for suggestion reasons in academic settings and iii) examples of academic systems and instruments the place techniques are incorporated.
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Extra resources for Recommender Systems for Technology Enhanced Learning: Research Trends and Applications
Sample text
We have used WEKA [15] to generate and test models for the classification of resources between good and not-good, and among good, average and poor resources through the following classification algorithms: J48, SimpleCart, PART, Multilayer Perceptron Neural Network and Bayesian Network. Tables 3, 4 and 5 present the results of these tests. For all tests we have used the same metrics previously identified as potential indicators of quality for each subset (Table 2). 1 2 3 4 5 6 7 8 9 10 N Good and not-good Good, average and poor Good and not-good Good, average and poor Good and not-good Good, average and poor Good and not-good Good, average and poor Good and not-good Good, average and poor Classes in the model All models were tested with 13 metrics.
Resources with ratings below the first tercile are classified as poor, resources with ratings equal or higher the first tercile and lower than the second tercile are classified as average, and resources with ratings equal or higher the second tercile are classified as good. The classes of quality average and poor were then joined in another class called not-good and were used as the output reference for generating and testing models for automated quality assessment of the resources First Experiment: Statistical Profiles of Highly-Rated Resources The collected sample contained 6,470 learning resources classified into 7 different disciplines and 9 distinct types of material, thus totalizing 63 different classes of possible learning object profiles.
In the future, we will examine refinements of the way QS is calculated, and examine the accuracy of other sentiment analysis methods. User evaluations will be an important part of this process, as we should examine if the unavoidable increased delay that will be introduced from more complex sentiment analysis methods will have a negative impact on user acceptance despite offering better recommendations. Regarding the evaluation of the quality-centric recommender system itself, it was revealed that the introduction of more elaborate analysis on user-provided information can produce a more accurate picture for the individual opinions expressed.
