@article{MAKHILLJEAS2018132417242,
    title = {Recommendation System to Improve Time Management for People in
Education Environments},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {24},
    pages = {10182-10193},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.10182.10193},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.10182.10193},
    author = {Samaher,Ahmed and},
    keywords = {education
environment,time management,sequence of occurrences,recommendation,Human behavior analysis,MAE},
    abstract = {The subject of time management is important and useful in different fields of our life and society as
a whole. It is considered as an invaluable factor if utilized correctly. This study aims to design a Time
Management Recommendation System (TMRS) for Educational Environment (EE) to provide their students,
employees and academic staff an effective way to exploit their time. We will build a system consist of 6 steps:
set a questionnaire, it consists of 50 questions. These questions explain how each person deals with the time,
distribute the questionnaire among target categories (student, academic staff and employees) in EE using a web
link and collect their responses in real time, pre-processing target categories responses by transforming from
them from descriptive to digital answer to help deal with these data that will be used to build the fitted model
and the most important questions were determined in this phase. Design suitable model for all the 800 response
samples used in testing for the all three group of people in EE in this step, design model shows the relationship
between the response of academic staff and employees, other model show the relationship between the
response of academic staff and students, final model show the fitted model among the response of academic
staff employees and students. The TMRS Model performance was evaluated using two types of statistical
measures and each one has 5 measures. By another words, the model accuracy will be tested using ten
measures, these measures include five measures generated by a confusion matrix, namely: Accuracy (AC), recall
or True Positive rate (TP), Precision (P), F-measure and (considers both precision and recall) and Fb. In addition,
five error measures, namely: Maximum Error (ME, Root Mean Squared Error (RMSE), Mean Squared Error
(MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Finally, generate different
recommendations compactable with the fitted model, these recommendations split into four groups. The
findings and analysis of this research can provide a way for the target categories to gather useful information
about probable trouble areas, possible areas for development and what to do to manage, use, plan, exploit and
get the maximum value from their time.}
    }