An Active Learning Approach for Teaching Undergraduate Heat Transfer

2008 ◽  
Author(s):  
Michael B. Pate

A novel method for teaching undergraduate heat transfer by using an active learning approach is presented. The method focuses on minimizing lecture time and maximizing student engagement. Learning is achieved by forming small groups of two to three students who then work on in-class graded assignments.

2020 ◽  
Vol 5 (36) ◽  
pp. 168-181
Author(s):  
Siti Nor Ani Azaman ◽  
Ezyana Kamal Bahrin ◽  
Nor Hafizah Ahmad Kamarudin ◽  
Marina Mohd Top @ Mohd Tah ◽  
Nadiatul Hafiza Hassan ◽  
...  

A growing revolution is underway in teaching introductory science to foundation studies. Recent educational research explains that traditional teaching approaches in large classes often fail to reach many students. To address this problem, we conducted an intensive station rotation-based workshop called “Bio Made Easy” for a group of students who obtained F grade in the first Biology assessment, N = 120. The workshop was designed to improve students’ understanding of selected Biology topics by providing simple examples, analogy, and explanation of the concept using various active and interactive approaches. The goal was to change the students’ perception of biology and deliver the content within a short period of time. The students were divided into small groups and required to complete all stations conducted by different instructors. For each station, students were directed to perform activities that required them to actively participate, interact, and discuss among the group members. A comparison was made between their performance during the first test and the second test of the semester. From F grade in the first test, about 88.3% of the students accomplished higher performance grades in the test 2 assessment. This reflects an encouraging sign that active-learning practice and direct engagement of students in the station rotation-based learning approach improve student performance in biology subjects and serve as one of the strategies to motivate students for better grades.


2017 ◽  
Vol 11 (2) ◽  
pp. 28 ◽  
Author(s):  
Paula Funnell

One of the key challenges in Information Literacy (IL) teaching in higher education is ensuring student engagement. As such, active learning approaches are encouraged in order to maximise student participation and interaction with the teaching. The use of audience response systems (ARSs) is one active learning approach which is being used increasingly in IL teaching. The purpose of this study is to investigate the effectiveness of ARSs in terms of increased engagement and student learning. Previous research has explored the use of ARSs as an active learning approach in comparison to traditional lectures, but this study aims to specifically examine the effectiveness of these tools as part of an active learning pedagogy. Most existing studies have looked at a single ARS, usually clickers. With an increase in availability and functionality of online tools, and discussions at a university level about moving to a single system which makes use of students’ own devices, this study also aims to compare the effectiveness of clickers and online ARSs. A controlled study was carried out on two cohorts of medical students at Queen Mary University of London comparing the use of clickers, online response tools, or a mixture of the two, to teaching without ARSs. Class observation and student evaluation were used to measure student engagement, and quizzes and student confidence levels to measure student learning. Results of the study showed that ARSs, when used as part of an active learning pedagogy, are an effective tool in terms of increasing student engagement, and have a generally positive impact on student learning, with online tools being slightly more effective than clickers. The study provides evidence which can be used by IL practitioners to help integrate ARSs into their teaching as well as inform institutional decisions on the use of these tools.


2019 ◽  
Vol 9 (4) ◽  
pp. 63
Author(s):  
Laurie O Campbell ◽  
Samantha Heller ◽  
Ronald F DeMara

In this case study, an active learning approach to exam preparation in engineering was investigated. The Learner Video Thumbnailing (LVT) strategy incorporated video blogs (vlogs) to reinforce course content. In this innovative method, students voluntarily choose one of two roles as either the role of a spectator (watching the vlogs) n=69 or the role of a vlogger (creating the vlogs) n=8 to earn extra credit on a formative exam. Data collected in this study included the vlogs, scores on the achievement questions, and a post-interview of the vloggers. Differences in video development by gender were identified. The use of the LVT approach promoted improved achievement and student engagement.


Author(s):  
Delismar Delismar

In classical learning approach, conventional lecture method is commonly used by teachers in implementing learning process in classes.  The teacher becomes the main source of learning.  The current student’s habit that tends to be passive and individualistic resulted in a passive and monotone learning.      To overcome these problems, I was interested to implement the model of numbered heads together in learning Physics in the Class VII B of SMP Negeri 5 Kota Jambi. The purpose of this learning approach is to enable students to develop cooperative skill and more active learning of physics and to improve learning results. This research is a class action research, which were performed in two cycles.  All students’ activities in the class were observed and recorded in observation sheet, consisting of teacher observation sheet and student observation sheet. To find out the learning outcomes, formative test was performed using a written instrument form.  The results show the increase of students’ discipline, cooperation, liveliness, timeliness in learning Physics.  In addition, the learning model also increases the students’ learning outcomes. The average learning results increased to 75.38 (increase 3.25 points).  To conclude, the implementation of Number Head Together increase students’ discipline, cooperation, activities, and timeliness.  The model also increase the Physics learning outcome of student in SMP Negeri 5 Kota  Jambi.


2020 ◽  
Vol 6 (4) ◽  
pp. 266-273
Author(s):  
Jeanita W. Richardson

This active learning exercise is designed to deconstruct the impact of social determinants through the assumption of randomly selected personas. As an active learning exercise, it provides opportunities for discussion, problem solving, writing, and synthesis, while incorporating multiple learning style preferences. Part 1 involves assessing the individual social determinants at work. Part 2 involves exploring ways said determinants can enhance community health through collaboration. Assumption of personas unlike one’s own facilitates an open discussion of social position and ranges of factors influential to health without potentially evoking a sense of defensiveness associated with personal privilege (or the lack thereof).


2017 ◽  
Vol 48 (2) ◽  
pp. 709-732 ◽  
Author(s):  
Patrick Thiam ◽  
Sascha Meudt ◽  
Günther Palm ◽  
Friedhelm Schwenker

Author(s):  
Xiang Lin ◽  
Haoran Liu ◽  
Zhi Wei ◽  
Senjuti Basu Roy ◽  
Nan Gao

2021 ◽  
Vol 143 (8) ◽  
Author(s):  
Opeoluwa Owoyele ◽  
Pinaki Pal ◽  
Alvaro Vidal Torreira

AbstractThe use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.


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