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Grp#2_Preproposal.pdf
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LabLove: A Recommender System for Laboratory Subjects in SLU using
Performance-based Algorithm
Background of the Study
Recommender systems have been used to improve learners' experience. It has been
used to properly manage learning resources in Morocco (Souabi et al., 2021). Nationally, it has
also been used to manage instructional resources with the use of collaborative filtering
technology (Li, 2022).
However, In a previous research conducted by the researchers of this paper, have found
that not all students are in favor of agreeing that laboratory subjects they took are educationally
effective in their teaching strategy. Thus there is a need to improve the teaching strategies of
laboratory subjects. However, to combat the educational deficiency of the students, the ECE
faculty conducts the SLU SEA-ECE Advising Program. The Advising Program is usually
accomplished professors meeting students every term and offering advice to students that are
academically failing. This is only effective when a student's grade is already available at the
portal, and the development of classroom interaction can be attributed to strengthening and
increasing student learning. So, to aid the professors of SLU in addressing the educational
deficiency, particularly in the Laboratory Subjects field, there is a need for a tool that can
automatically assess the grade of the class when it is already available.
Objective of the Study
This study is therefore undertaken to prove that an portal-integrated recommender
system using Performance-based Algorithm is a good and helpful system and in improving
classroom conduct of Laboratory Subjects in SLU.
Proposed Project
The recommender system is developed as an integrated tool to the SLU Portal capable
of displaying a notification, as well as generating a document page of the recommended action
pertaining to the improvement of the classroom conduct by implementing a Performance-based
Filtering approach.
The Performance-based filtering is based on the correlation between the grade of the
students and the teaching style being practiced. The tool itself is integrated as a separate tab in
the SLU Portal.
At the Recommender tab page, the user (teacher) selects which of his/her classes
he/she wishes to either generate a “class improvement recommendation” of, or update the
“teaching style” profile for that particular class. In the teaching style option, the teacher will be
inputting what teaching style was used at that current term. The recommendation would not be
generated when no profile has been submitted. When the generate recommendation option is
selected, the Algorithm will read the grade of the current term and the previous term if available.
The Algorithm will then decide if the teaching method is effective or not, and then recommend
by indicating in the generated document a teaching method to add, remove, or change.
References:
Souabi, S., Retbi, A., Idrissi, M. K., & Bennani, S. (2021). Recommendation Systems on
E-Learning and Social Learning: A Systematic Review. Electronic Journal of e-Learning,
19(5), pp432-451. https://doi.org/10.34190/ejel.19.5.2482
Li, J. (2022). A Recommendation Model for College English Digital Teaching Resources Using
Collaborative Filtering and Few-Shot Learning Technology. Computational Intelligence
and Neuroscience, 2022, 1–10. https://doi.org/10.1155/2022/1233057
“Investigating the Effectiveness of Simulators in Enhancing Student Engagement and
Motivation in Learning Concepts and Theories”
Background of the Study
In science and engineering education, hands-on experiments play a very important role
in providing students with practical experience as well as the opportunity to apply theoretical
concepts to real-world situations. However, there are a number of possible downsides to
hands-on experiments. Some include high materials and equipment costs, safety concerns, and
limited access to equipment. As such, with the advancement of technology, simulators emerged
and became a promising alternative to hands-on experiments. Simulators offer a safe and
cost-effective way to carry out experiments, especially when there is a need for complex and
limited equipment (Talan, 2021).
Depending on certain real-world situations, it has potential drawbacks. Some Simulators
are not perfect as they only give the ideal results of an experiment, which means that they are
not able to simulate exactly how things behave in the real world. Since the use of simulators is a
different approach to conducting experiments, it has a different level of engagement compared
to doing experiments hands-on (Olga, 2022). The level of engagement it offers affects how
motivated the students are in learning concepts and theories. These drawbacks are directly
related to the student's academic performance.
Objective of the Study
To examine the level of engagement that simulators offer compared to hands-on
experiments and how this affects students' motivation to learn concepts and theories.
Proposed Project
Simulators play a crucial role in Electronics Engineering, providing benefits such as
design and testing, training and education, and cost savings. However, there is limited research
on the engagement of simulation education in this program. To address this gap, our proposed
project involves a) designing and conducting a controlled experiment in a classroom setting,
where two groups of students will be exposed to either a hands-on experiment or a simulator for
the same concept. This allows for a direct comparison between the two methods and provides
quantitative data that can inform best practices in teaching; b) use a pre-test and post-test
design to measure the level of engagement and motivation of students in both groups. The
pre-test will be given before the experiment or simulation, and the post-test will be given after
the activity. These tests will serve as measuring device to see the changes in student
engagement and motivation, control for individual differences, provides quantitative data,
enhances validity and reliability, and supports evidence-based recommendations for educators
and instructors; and lastly, c) administer a survey to the students to collect their feedback on
their experience with the experiment or simulation and to gauge their motivation to learn the
concept. The survey shall provide valuable qualitative data, allows the gathering of opinions and
perceptions, enhances validity and reliability, informs best practices, and supports a
student-centered approach to teaching.
References:
Simulation-based learning in Higher Education: A meta-analysis. (n.d.). Retrieved April 22,
2023, from https://journals.sagepub.com/doi/10.3102/0034654320933544
Talan, T. (2020, November 30). The effect of simulation technique on academic achievement: A
meta-analysis study. International Journal of Technology in Education and Science.
Retrieved April 22, 2023, from https://eric.ed.gov/?id=EJ1286535
"Simulation-Based Design Optimization of Power Electronics Systems: Development of a
Prototype and Performance Evaluation"
Background of the Study
Simulation-based design optimization of power electronics systems is a topic that has
gained increasing attention in recent years due to the demand for efficient and reliable power
electronics systems in various applications. In this research, the authors aimed to develop a
prototype of a simulation-based design optimization tool for power electronics systems and
evaluate its performance.
To start, according to Li et al. (2018) presented a simulation-based design optimization
approach for a high-power density DC-DC converter using a hybrid genetic algorithm and
response surface methodology. The results showed that the optimized design achieved a higher
power density and lower losses than the initial design. In another research by Yang et al. (2019),
a simulation-based design optimization framework was proposed for a grid-connected inverter
system with an LCL filter. The authors used a surrogate model based on artificial neural
networks and genetic algorithms for optimization. The results demonstrated the effectiveness of
the proposed methodology in improving efficiency and reducing the cost of the system.
Objective of the Study
This study would entail the creation of a simulation-based optimization tool for
developing and assessing power electronics systems. The device would employ simulation
models to produce and evaluate design options and improve system performance based on
pre-defined objectives like efficiency, power density, and thermal management. The research
might include constructing and testing the enhanced design of a physical power electronics
system to assess its performance and compare it to standard design methodologies.
Proposed Project
The operation can be divided into 5 steps, which can be summarized as: Develop
simulation models, Define objectives and constraints, Run simulations, Prototype development,
Performance evaluation.
The first step would be to develop models that accurately represent the behavior of the
power electronics system under various operating conditions; The next step would be to define
the goals and conditions for the design optimization; The simulation-based optimization tool
then generates and evaluates multiple design alternatives based on the defined objectives and
constraints. The device would use genetic or particle swarm optimization algorithms to search
the design space and efficiently identify optimal solutions; After identifying an optimized design,
the power electronics system prototype will be developed based on the simulation results; The
final step would involve evaluating the prototype's performance and comparing it with traditional
design approaches. The performance evaluation could include measurements of efficiency,
power density, thermal performance, and other relevant metrics.
References:
Design of a high power density DC-DC converter | IEEE conference ... (n.d.). Retrieved April 20,
2023, from https://ieeexplore.ieee.org/document/9479002
Wu, F., Sun, Z., Xu, W., Li, Z., & Lyu, J. (2021, December 16). A control parameters design
method with multi-constrains for an LCL-filtered grid-connected inverter in a weak grid.
Retrieved April 26, 2023, from
https://www.frontiersin.org/articles/10.3389/fenrg.2021.798793/full