Milestone - based assessment: an alternative continuous assessment strategy for laboratory learning outcomes

Engineering programs often feature units that contain a semester-long laboratory project, in which students complete an extended piece of work throughout the full duration of the semester. This paper presents an alternative assessment approach called “Milestone-Based Marking”. As students make incremental progress they can claim incremental marks, and are able to receive incremental feedback on their progress. Each of the milestones is rated for difficulty – Easy, Standard, Hard or Challenging. Easy milestones require less effort than Hard milestones, providing students with a clear guide as to how best to invest their time and effort. This approach changes the nature of the assessment from a purely summative process to a largely formative process. This approach has been used successfully across a number of units, with students indicating that they believe that the approach is fair, and that it better supports their learning

The relationship between first year engineering students' perceptions of workload and stress

Engineering degree programs are notorious for placing considerable demands upon their students. Balancing study and work is a challenge faced by an increasing number of undergraduate students. There is an implicit assumption that an increase in workload results in more stress for the students; however a closer examination of the situation reveals greater complexity. This paper presents data gathered in a semester-long weekly survey of first year engineering students. Students were asked to rate their stress levels and workload relative to normal, and they were also asked to give an absolute rating for their stress levels. Their self-reported levels of workload and stress are compared to each other and to the number of hours reported for study and paid employment. This comparison shows that while in general workload and stress are indeed linked, there is a substantial proportion of the cohort for whom these factors appear to be independent. In particular the link between absolute stress and workload appears weaker, suggesting that the issue may not be the actual level of stress, but rather the students’ perceptions of what constitutes a “normal” workload at a university level

Learning Styles a Potential Predictor of Student Achievement in Remote and Virtual Laboratory Classes

Remote and virtual laboratories are becoming increasingly prevalent as ways of providing engineering students with the laboratory learning experience. Previous literature suggests that there may be differences in the nature of these learning experiences, leading to difference in the learning outcomes achieved by students exposed to these different access modes.This paper investigates the impact of the students' preferred learning styles upon these changes in learning outcomes. This study shows that for some learning outcomes, the differences are not dependent solely upon access mode, but rather upon the interaction of access mode and learning style. Some styles are more susceptible to mode-based variations, whilst others show little change between the modes. This suggests that the students' preferred learning styles may be a potential diagnostic tool for determining which access mode will most enhance a given student's learning opportunities

Explaining Education to Engineers: Feedback Control Theory as a Metaphor

One of the barriers for engaging engineering faculty in the scholarship of learning and teaching is thechallenge of learning a new vocabulary. Becoming fluent in engineering education requires the acquisitionof new concepts and ideas that are often expressed in unfamiliar terms. Feedback control is a technical fieldcommon to a range of engineering disciplines that can be used as a model to help bridge the conceptual gapbetween traditional engineering and engineering education. Many of the key elements of engineering education can be represented by the elements of a feedback control system, with their behaviour in a learning environment paralleling their behaviour in a process control context. The feedback control model can be used to explain: the importance of timely feedback to students, the significance of assessment and evaluation in the learning process, the impact of learning styles upon learning outcomes, and the need for student-centered teaching approaches. While both fields have complexities that cannot be captured by simple models, the basic ideas can be explained simply. Feedback control metaphors make the basics accessible to a wider audience of engineering faculty

Special Session - It’s A Jungle Safari Out There: An Allegory for Learning to Navigate Academia

One of the key challenges at the beginning of an academic career is learning to live in the university working environment. Interactions between academics can be significantly different to those found in industry; and whilst many of the drivers for the organisation are similar, there are also some aspects that are unique to a university. Being able to successfully navigate through the office politics is exceptionally valuable to academic staff, both new and established. In order to do this, it is essential to understand what motivates both yourself and your colleagues. This workshop will use an African Safari as a metaphor for the academic working environment, allocating animals to represent some of the archetypes that are present within an Engineering department

An analysis of the lifeloads of first year engineering students

Professional degree programs, such as Engineering, are notorious for placing considerable demands upon their students. Balancing study and work is a challenge faced by an increasing number of undergraduate students. In order to assist students to manage this balance it is important to know how this compromise varies from student to student. This paper presents data gathered from first year engineering students regarding the hours they spend each week in study, paid employment and volunteer work. A substantial variation across the cohort is shown for all three factors. The data also shows variation in these factors with respect to the week of semester, with time commitments growing independently early in semester, and study somewhat taking the place of paid employment later in the semester

Wrapping Snakes for Improved Lip Segmentation

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Ethical Concerns And Responsible Use Of Generative Artificial Intelligence In Engineering Education

The use of educational technologies that use elements of machine learning (ML) and artificial intelligence (AI) are becoming common across the engineering education terrain. With the wide adoption of generative AI based applications, this trend is only going to grow. Not only is the use of these technologies going to impact teaching, but engineering education research practices are as likely to be affected as well. From data generation and analysis, to writing and presentation, all aspects of research will potentially be shaped. In this practice paper we discuss the ethical implications of the use of generative AI technologies on engineering teaching and engineering education research. We present a discussion of potential and futuristic concerns raised by the use of these technologies. We bring to the fore larger organizational and institutional issues and the need for a framework for responsible use of technology within engineering education. Finally, we engage with the current literature and popular writing on the topic to build an understanding of the issues with the potential to apply them in teaching and research practices