scholarly journals PEER FEEDBACK OVER TIME

Author(s):  
Julia N. Smith ◽  
Thomas A. O’Neill

Abstract –Given the ubiquity of teamwork in engineering education and industry1, developing teamwork skills in undergraduate students is a critical component of their training. This is supported by the inclusion of ‘individual and teamwork’ as a graduate attribute by the Canadian Engineering Accreditation Board2. The current work explores the development of teamwork skills through the use of multiple administrations of peer feedback, in order to explore the value of using several administrations and support past findings that have suggested increased administrations provide incremental improvements. Additionally, the paper discusses the use of an empirically validated and user-friendly tool used to deliver the peer feedback assessments. The results suggest that students find the tool easy to use and that they believed the feedback they received and gave was accurate and useful. Together, these results suggest that peer feedback, delivered using the ITPMetrics.com platform, is an effective and well-received method of fostering soft-skill development in engineering students.  

Author(s):  
Varghese Panthalookaran

Engineering soft skills are required of an engineer to excel in his/her career and profession. For engineering education institutions affiliated to a central university and working with a prescribed curriculum, it is often difficult to find time for real-world training of students in soft skills. The current paper summarizes the programs designed for undergraduate engineering students of an affiliated engineering institution in order to circumvent this problem. They integrate training in engineering soft skills with the regular academic schedule exerting minimal extra-loading for the students and ensuring individual attention. The distribution of training programs in time also facilitates natural and gradual development of the values, attitudes and soft-skills. Further, integration of soft skill training programs into regular academic schedule enhances the interest of students in academics.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Madison E. Andrews ◽  
Anita D. Patrick ◽  
Maura Borrego

Abstract Background Students’ attitudinal beliefs related to how they see themselves in STEM have been a focal point of recent research, given their well-documented links to retention and persistence. These beliefs are most often assessed cross-sectionally, and as such, we lack a thorough understanding of how they may fluctuate over time. Using matched survey responses from undergraduate engineering students (n = 278), we evaluate if, and to what extent, students’ engineering attitudinal beliefs (attainment value, utility value, self-efficacy, interest, and identity) change over a 1-year period. Further, we examine whether there are differences based on gender and student division, and then compare results between cross-sectional and longitudinal analyses to illustrate weaknesses in our current understanding of these constructs. Results Our study revealed inconsistencies between cross-sectional and longitudinal analyses of the same dataset. Cross-sectional analyses indicated a significant difference by student division for engineering utility value and engineering interest, but no significant differences by gender for any variable. However, longitudinal analyses revealed statistically significant decreases in engineering utility value, engineering self-efficacy, and engineering interest for lower division students and significant decreases in engineering attainment value for upper division students over a one-year period. Further, longitudinal analyses revealed a gender gap in engineering self-efficacy for upper division students, where men reported higher means than women. Conclusions Our analyses make several contributions. First, we explore attitudinal differences by student division not previously documented. Second, by comparing across methodologies, we illustrate that different conclusions can be drawn from the same data. Since the literature around these variables is largely cross-sectional, our understanding of students’ engineering attitudes is limited. Our longitudinal analyses show variation in engineering attitudinal beliefs that are obscured when data is only examined cross-sectionally. These analyses revealed an overall downward trend within students for all beliefs that changed significantly—losses which may foreshadow attrition out of engineering. These findings provide an opportunity to introduce targeted interventions to build engineering utility value, engineering self-efficacy, and engineering interest for student groups whose means were lower than average.


Author(s):  
Rod D. Roscoe ◽  
Samuel T. Arnold ◽  
Ashley T. Clark

Instruction and coursework that link engineering and psychology may enable future engineers to better understand the people they are engineering for (e.g., users and clients) and themselves as engineers (e.g., teammates). In addition, human-centered engineering education may empower engineering students to better solve problems at the intersection of technology and people. In this study, we surveyed students’ conceptions and attitudes toward human systems engineering. We aggregate responses across three survey iterations to discuss students’ knowledge and beliefs, and to consider instructional opportunities for introductory courses.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ermanno Cordelli ◽  
Paolo Soda ◽  
Giulio Iannello

Abstract Background Biological phenomena usually evolves over time and recent advances in high-throughput microscopy have made possible to collect multiple 3D images over time, generating $$3D+t$$ 3 D + t (or 4D) datasets. To extract useful information there is the need to extract spatial and temporal data on the particles that are in the images, but particle tracking and feature extraction need some kind of assistance. Results This manuscript introduces our new freely downloadable toolbox, the Visual4DTracker. It is a MATLAB package implementing several useful functionalities to navigate, analyse and proof-read the track of each particle detected in any $$3D+t$$ 3 D + t stack. Furthermore, it allows users to proof-read and to evaluate the traces with respect to a given gold standard. The Visual4DTracker toolbox permits the users to visualize and save all the generated results through a user-friendly graphical user interface. This tool has been successfully used in three applicative examples. The first processes synthetic data to show all the software functionalities. The second shows how to process a 4D image stack showing the time-lapse growth of Drosophila cells in an embryo. The third example presents the quantitative analysis of insulin granules in living beta-cells, showing that such particles have two main dynamics that coexist inside the cells. Conclusions Visual4DTracker is a software package for MATLAB to visualize, handle and manually track $$3D+t$$ 3 D + t stacks of microscopy images containing objects such cells, granules, etc.. With its unique set of functions, it remarkably permits the user to analyze and proof-read 4D data in a friendly 3D fashion. The tool is freely available at https://drive.google.com/drive/folders/19AEn0TqP-2B8Z10kOavEAopTUxsKUV73?usp=sharing


Author(s):  
TMGP Duarte ◽  
AM Lopes ◽  
LFM da Silva

Understanding how the academic performance of first year undergraduate students is influenced by home, personal and institutional factors is fundamental to delineate policies able to mitigate failure. This paper investigates possible correlations between the academic performance of students at the end of high school with their achievements at the end of first year university. Data for students in the Integrated Master in Mechanical Engineering (MIEM) program within the Faculty of Engineering at the University of Porto are analysed for the period 2016/2017 to 2019/2020. The students’ performance is measured by two metrics and the students are structured as a whole and by groups, according to their gender (Male/Female), type of secondary school (Public/Private), living place (Away/Home) and the rank of MIEM in their application list of options (Option 1/Option 2–6). The information is organized statistically and possible correlations between the data are investigated. The analysis reveals limited correlation between the two metrics, meaning that all students may exhibit good or poor results at the end of first year in MIEM, independent of their status at entrance. An unanticipated pattern is exhibited for the group Option 2–6, since it shows that, despite entering into MIEM without top application marks, the students in this group can perform as well as the others. This behavior is consistent over time.


2012 ◽  
Vol 60 ◽  
pp. 507-511 ◽  
Author(s):  
Siti Kartom Kamaruddin ◽  
Noorhisham Tan Kofli ◽  
Manal Ismail ◽  
Abu Bakar Mohammad ◽  
Mohd Sobri Takriff
Keyword(s):  

2001 ◽  
Vol 26 (4) ◽  
pp. 367-380 ◽  
Author(s):  
Phyllis Laybourn ◽  
Judy Goldfinch ◽  
Jennifer Graham ◽  
Lucy MacLeod ◽  
Sheila Stewart

2021 ◽  
pp. 109442812199322
Author(s):  
Ali Shamsollahi ◽  
Michael J. Zyphur ◽  
Ozlem Ozkok

Cross-lagged panel models (CLPMs) are common, but their applications often focus on “short-run” effects among temporally proximal observations. This addresses questions about how dynamic systems may immediately respond to interventions, but fails to show how systems evolve over longer timeframes. We explore three types of “long-run” effects in dynamic systems that extend recent work on “impulse responses,” which reflect potential long-run effects of one-time interventions. Going beyond these, we first treat evaluations of system (in)stability by testing for “permanent effects,” which are important because in unstable systems even a one-time intervention may have enduring effects. Second, we explore classic econometric long-run effects that show how dynamic systems may respond to interventions that are sustained over time. Third, we treat “accumulated responses” to model how systems may respond to repeated interventions over time. We illustrate tests of each long-run effect in a simulated dataset and we provide all materials online including user-friendly R code that automates estimating, testing, reporting, and plotting all effects (see https://doi.org/10.26188/13506861 ). We conclude by emphasizing the value of aligning specific longitudinal hypotheses with quantitative methods.


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