2 research outputs found
Mining Student Responses to Infer Student Satisfaction Predictors
The identification and analysis of student satisfaction is a challenging
issue. This is becoming increasingly important since a measure of student
satisfaction is taken as an indication of how well a course has been taught.
However, it remains a challenging problem as student satisfaction has various
aspects. In this paper, we formulate the student satisfaction estimation as a
prediction problem where we predict different levels of student satisfaction
and infer the influential predictors related to course and instructor. We
present five different aspects of student satisfaction in terms of 1) course
content, 2) class participation, 3) achievement of initial expectations about
the course, 4) relevancy towards professional development, and 5) if the course
connects them and helps to explore the real-world situations. We employ
state-of-the-art machine learning techniques to predict each of these aspects
of student satisfaction levels. For our experiment, we utilize a large student
evaluation dataset which includes student perception using different attributes
related to courses and the instructors. Our experimental results and
comprehensive analysis reveal that student satisfaction is more influenced by
course attributes in comparison to instructor related attributes.Comment: Seventh International Conference on Learning and Teaching in
Computing and Engineering (LaTiCE'20
MoParkeR : Multi-objective Parking Recommendation
Existing parking recommendation solutions mainly focus on finding and
suggesting parking spaces based on the unoccupied options only. However, there
are other factors associated with parking spaces that can influence someone's
choice of parking such as fare, parking rule, walking distance to destination,
travel time, likelihood to be unoccupied at a given time. More importantly,
these factors may change over time and conflict with each other which makes the
recommendations produced by current parking recommender systems ineffective. In
this paper, we propose a novel problem called multi-objective parking
recommendation. We present a solution by designing a multi-objective parking
recommendation engine called MoParkeR that considers various conflicting
factors together. Specifically, we utilise a non-dominated sorting technique to
calculate a set of Pareto-optimal solutions, consisting of recommended
trade-off parking spots. We conduct extensive experiments using two real-world
datasets to show the applicability of our multi-objective recommendation
methodology.Comment: 6 pages, 5 figure