scholarly journals Investigating First-Year Engineering Students' Educational Technology Use and Academic Achievement: Development and Validation of an Assessment Tool

2016 ◽  
Author(s):  
Leroy Long
Keyword(s):  
Academic Achievement ◽  
Educational Technology ◽  
Technology Use ◽  
Engineering Students ◽  
Assessment Tool ◽  
First Year ◽  
Development And Validation

Role of Emotional Intelligence in Academic Achievement

2014 ◽  
pp. 255-263 ◽  
Author(s):  
Tripti Singh ◽  
Manish Kumar Verma ◽  
Rupali Singh
Keyword(s):  
Academic Achievement ◽  
Emotional Intelligence ◽  
Social Intelligence ◽  
Engineering Students ◽  
School Level ◽  
First Year ◽  
First Year Students ◽  
Academic Achievements ◽  
Emotional Intelligence Inventory

The purpose of this study is to see whether there is a relationship between emotional intelligence and academic achievement. The study respondents were B.Tech first year students from the Agra region. Sampling is stratified, making sure that gender, race, socioeconomic status, and abilities are appropriately represented. The respondents are given Emotional Intelligence Inventory (EII–MM), developed by S. K. Mangal and Shubhra Mangal. It consists of 100 items under four scales .The analysis suggests that there is a significant relationship between Emotional Intelligence and Academic Achievement. IQ alone is no more the measure for success; emotional intelligence, social intelligence, and luck also play a big role in a person's success. This study contributes in acknowledging the fact that even engineering students’ academic achievements are attached with Emotional intelligence. Thus, teaching emotional and social skills only at the school level is not sufficient; this can be taught in engineering studies, as well for accomplishing high academic achievements.

Contribution of Intelligence and Cognitive-Affective Variables to University Grades among African, Indian, and White Engineering Students in South Africa

2005 ◽  
Vol 5 (1) ◽  
pp. 25-46 ◽  
Author(s):  
Mervyn Skuy ◽  
Melissa Skuy
Keyword(s):  
Academic Achievement ◽  
Academic Performance ◽  
Engineering Students ◽  
Intellectual Ability ◽  
First Year ◽  
African Students ◽  
Affective Variables ◽  
African Group ◽  
Two Measures ◽  
Intelligence Test Scores

In previous studies significant differences in measured intelligence between African and non-African first year engineering students have been found. Intellectual ability was found to correlate with academic performance, and black studednts had higher dropout and failure rates and performed less well than did their non-African counterparts. Given the low magnitude (r = 0.3), albeit significant, of the correlation between intelligence and academic performance, the question arose of the role of non-intellective factors, relative to intelligence, in determining academic performance of engineering students at University. Accordingly, 93% (n=100) of the second year Chemical and Metallurgical Engineering class were assessed on two measures of intellectual ability, and on measures of self concept, motivation, study attitudes and strategies, anxiety, locus of control, and autonomy. Whereas the intelligence test scores of non-African students (n=36) were significantly higher than those of African students (n=64), this was not the case for any of the non-intellective measures, or for academic achievement. Moreover, although the intellectual measures did not yield significant correlations with academic achievement, certain of the non-intellective measures did, and were able to differentiate between high and low academic performers. This was particularly true for the African group, suggesting that non-intellective variables can contribute significantly to academic performance, particularly in mitigating the effects of lower IQ.

scholarly journals Member Perceptions as a Parsimonious Representation of Collaboration Viability

2018 ◽  
Author(s):  
Benjamin Heslop ◽  
Kylie Bailey ◽  
Jonathan Paul ◽  
Liz Stojanovski
Keyword(s):  
Engineering Students ◽  
Assessment Tool ◽  
Peer Assessment ◽  
Empirical Support ◽  
Assessment Instrument ◽  
First Year ◽  
Rater Bias ◽  
Assessment Data ◽  
Student Responses ◽  
Systems Perspective

The PILAR model provides a dynamical systems perspective on collaboration. Two studies are performed using peer assessment data, both testing empirical support for the five Pillars (prospects, involved, liked, agency, respect) that constitute member’s perceptions of collaboration viability. The first study analyses peer-assessment data collected online from 458 first-year engineering students (404 males; 54 females). A nine-item instrument was inherited from past year’s usage in the course, expanded with four additional items to elaborate upon the agency and liked Pillars. Exploratory factor analysis was conducted on student responses to test whether they thematically aligned to constructs consistent with the five Pillars. As anticipated, twelve of the thirteen items grouped into five components, each aligned with a Pillar, providing empirical evidence that the five Pillars represent perceptions of collaboration. The second study replicated the first study using a retrospective analysis of 87 items included in the Comprehensive Assessment of Team Member Effectiveness (CATME) peer-assessment tool. The associated factor analyses resulted in five components and conceptual alignment of these components with Pillars was evident for three of five CATME components. We recommend a peer-assessment instrument based upon PILAR as potentially more parsimonious and reliable than an extensive list of behaviours, such as employed by CATME. We also recommend including items that target inter-rater bias, which is aligned with the liked Pillar, that instruments such as CATME exclude.

REVERSE ENGINEERING: TENSION RATCHET DISSECTION PROJECT

2011 ◽  
Author(s):  
Jeremiah Vanderlaan ◽  
Josh Richert ◽  
James Morrison ◽  
Thomas Doyle
Keyword(s):  
Reverse Engineering ◽  
Engineering Students ◽  
Measurement Techniques ◽  
First Year ◽  
Stepping Stones ◽  
First Year Students ◽  
Final Project ◽  
Undergraduate Study ◽  
Solid Edge ◽  
Part Modeling

We are a group of engineering students, in our first year of undergraduate study. We have been selected from one thousand first year students and have competed and won the PACE competition. All engineers share a common general first year, but we have been accepted into Civil and Mechanical engineering. This project was assigned as the final project in the Design and Graphics course. The project we are tasked with, called the Cornerstone Design Project, is to first dissect a product, discover how it works, dimension each part and create a fully assembled model using CAD software (Solid Edge V20 in our case). As part of discovering how it works we must benchmark it so the device can be compared with competing products. The goal of the project is to develop a full understanding of part modeling and assembly in Solid Edge, learn proper measurement techniques, and learn the process of reverse engineering and product dissection. All of these tasks were stepping stones to help us fully understand how the device, and all its components, work.

Implementation of the Second-year course in an Engineering Professional Spine

2018 ◽  
Author(s):  
Umar Iqbal ◽  
Deena Salem ◽  
David Strong
Keyword(s):  
Learning Outcomes ◽  
Research University ◽  
Engineering Students ◽  
Employability Skills ◽  
First Year ◽  
Computer Engineering ◽  
Core Courses ◽  
Second Year ◽  
First Time ◽  
Academic Year

The objective of this paper is to document the experience of developing and implementing a second-year course in an engineering professional spine that was developed in a first-tier research university and relies on project-based core courses. The main objective of this spine is to develop the students’ cognitive and employability skills that will allow them to stand out from the crowd of other engineering graduates.The spine was developed and delivered for the first time in the academic year 2010-2011 for first-year general engineering students. In the year 2011-2012, those students joined different programs, and accordingly the second-year course was tailored to align with the different programs’ learning outcomes. This paper discusses the development and implementation of the course in the Electrical and Computer Engineering (ECE) department.

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