Student Collaboration in IT and Engineering Education

Universities are locations of knowledge gathering and creation. Within teaching approaches, collaborative learning is a practice whereby students work together through participation and interaction to synthesise knowledge together (Paulus 2005). Whilst group work is quite popular in technical fields true collaborative learning, as opposed to cooperation, has traditionally been considered easier to implement in fields like the arts rather than in the technical fields due in part to a greater focus on group synthesis tasks in the former and application tasks in the latter. Further, the collaborative tools used in online education environments are touted as the cure-all for implementing collaborative learning, however, collaboration is often not experienced to the fullest extent in these environments and does not happen automatically (e.g. Hathorn & Ingram 2002; Kim 2013). Previous studies in engineering have shown positive relationships between students’ reporting of their own informal collaboration with their confidence in their learning of course material, knowledge building behaviours, and their course grade (Stump et al 2013). Also gender differences have been found in use of collaborative learning activities (Stump et al 2013) which suggests its use may benefit some underrepresented students. However to do this, learning design, scaffolding and assessment frameworks amongst other factors must be considered by educators for effective collaboration (e.g. Kim 2013; Kurnaz, Erg ̈un, & Ilgaz 2018). Various works (e.g. Göl & Nafalski 2007; Finger et al 2005) highlight the integration of collaboration in studio and project-based learning, but it can also be integrated into more conventional lab-experiment type subjects (Schaf et al 2009). This work examines the psychological underpinnings, benefits, problems, and practice of collaborative learning with a particular focus on its potential for IT and engineering education at a technical University moving to studio-based learning. The research focus is how collaborative learning is being implemented in IT and engineering and how its use can be improved given the industrial, academic and learning context in the case study University. With the growing push to incorporate these approaches into engineering and IT, it is important that the instructors and students have the tools to best engage in effective collaboration. Selecting these tools may depend on the learning context, the content type and the lecturer’s style

Student Collaboration in IT and Engineering Education

Universities are locations of knowledge gathering and creation. Within teaching approaches, collaborative learning is a practice whereby students work together through participation and interaction to synthesise knowledge together (Paulus 2005). Whilst group work is quite popular in technical fields true collaborative learning, as opposed to cooperation, has traditionally been considered easier to implement in fields like the arts rather than in the technical fields due in part to a greater focus on group synthesis tasks in the former and application tasks in the latter. Further, the collaborative tools used in online education environments are touted as the cure-all for implementing collaborative learning, however, collaboration is often not experienced to the fullest extent in these environments and does not happen automatically (e.g. Hathorn & Ingram 2002; Kim 2013). Previous studies in engineering have shown positive relationships between students’ reporting of their own informal collaboration with their confidence in their learning of course material, knowledge building behaviours, and their course grade (Stump et al 2013). Also gender differences have been found in use of collaborative learning activities (Stump et al 2013) which suggests its use may benefit some underrepresented students. However to do this, learning design, scaffolding and assessment frameworks amongst other factors must be considered by educators for effective collaboration (e.g. Kim 2013; Kurnaz, Erg ̈un, & Ilgaz 2018). Various works (e.g. Göl & Nafalski 2007; Finger et al 2005) highlight the integration of collaboration in studio and project-based learning, but it can also be integrated into more conventional lab-experiment type subjects (Schaf et al 2009). This work examines the psychological underpinnings, benefits, problems, and practice of collaborative learning with a particular focus on its potential for IT and engineering education at a technical University moving to studio-based learning. The research focus is how collaborative learning is being implemented in IT and engineering and how its use can be improved given the industrial, academic and learning context in the case study University. With the growing push to incorporate these approaches into engineering and IT, it is important that the instructors and students have the tools to best engage in effective collaboration. Selecting these tools may depend on the learning context, the content type and the lecturer’s style

Frequency Dependent Specific Heat from Thermal Effusion in Spherical Geometry

We present a novel method of measuring the frequency dependent specific heat at the glass transition applied to 5-polyphenyl-4-ether. The method employs thermal waves effusing radially out from the surface of a spherical thermistor that acts as both a heat generator and thermometer. It is a merit of the method compared to planar effusion methods that the influence of the mechanical boundary conditions are analytically known. This implies that it is the longitudinal rather than the isobaric specific heat that is measured. As another merit the thermal conductivity and specific heat can be found independently. The method has highest sensitivity at a frequency where the thermal diffusion length is comparable to the radius of the heat generator. This limits in practise the frequency range to 2-3 decades. An account of the 3omega-technique used including higher order terms in the temperature dependency of the thermistor and in the power generated is furthermore given.Comment: 17 pages, 15 figures, Substantially revised versio

Landfill Lifespan Estimation: A Case Study

Municipal Solid Waste (MSW) management is one of the most serious environmental challenges facing the world at large due to the decomposing effect from the toxic gases being released into the environment by the MSW. The siting of landfill in any environment is a vital consideration that must be looked at due to the many factors such as the lifespan of the landfill, site selection, design, construction, operation and management. For this reason, it is important to estimate the lifespan of landfill accurately so as to explore the risk involved in acquiring new lands for landfills. Moreover, it is also necessary to consider proper methodology for estimating the lifespan of landfills. Based on these factors enumerated, various researchers have performed several laboratory tests in order to conclude on appropriate model that could be used to predict the lifespan of modern landfills. Mathematical models or expressions have also been suggested in literature as an alternative approach to the estimation of landfills lifespan. This research used the future value of money equation to estimate the lifespan of the Aboso landfill in Tarkwa, Ghana. The result showed that the landfill could operate for the next twelve years before it could exhaust its usefulness. Keywords: Landfill, Municipal Solid Waste, Lifespan Estimatio

Publication bias and the canonization of false facts

In the process of scientific inquiry, certain claims accumulate enough support to be established as facts. Unfortunately, not every claim accorded the status of fact turns out to be true. In this paper, we model the dynamic process by which claims are canonized as fact through repeated experimental confirmation. The community's confidence in a claim constitutes a Markov process: each successive published result shifts the degree of belief, until sufficient evidence accumulates to accept the claim as fact or to reject it as false. In our model, publication bias --- in which positive results are published preferentially over negative ones --- influences the distribution of published results. We find that when readers do not know the degree of publication bias and thus cannot condition on it, false claims often can be canonized as facts. Unless a sufficient fraction of negative results are published, the scientific process will do a poor job at discriminating false from true claims. This problem is exacerbated when scientists engage in p-hacking, data dredging, and other behaviors that increase the rate at which false positives are published. If negative results become easier to publish as a claim approaches acceptance as a fact, however, true and false claims can be more readily distinguished. To the degree that the model accurately represents current scholarly practice, there will be serious concern about the validity of purported facts in some areas of scientific research

Evaluating In-house Work Integrated Learning Experiences Using the Business Model Canvas

CONTEXT The school of Professional Practice and Leadership at UTS set up Optik Consultancy to provide students unable to access internships, with engineering projects set up by industry partners in a simulated workplace. In 2021, in the midst of the COVID-19 crisis, 120 students (85 international and 35 domestic) completed Work Integrated Learning (WIL) in this manner. This was the 5th iteration of the project with the number of students increasing each year. This model has the potential to be extended to other groups such as refugees needing existing qualifications validated, or engineers returning to the workplace after an extended absence. To do this successfully, it is necessary to ensure the program meets participants’ requirements. This requires recognition of the complexity of the program and the development of a framework to ensure all elements that make a successful program are in place. PURPOSE OR GOAL This paper analyses the Optik Consultancy through the lens of the ‘Business Model Canvas’ (Osterwalder & Pigneur (2010). As illustrated by Kline et al (2017), this framework can be adapted to design a template to meet the specific needs of educational projects. We aim to analyse the main activities and processes of the Optik Consultancy and redesign the Business Model Canvas for WIL engineering projects to identify the elements necessary for designing a similar project in other settings. APPROACH Firstly, we will investigate the Optik Consultancy through the lens of the ‘Business Model Canvas. This will enable us to identify key areas relevant to a simulated internship program in order to form an engineering WIL canvas. This canvas will explain what we do, how we do it and why. We will then apply our new canvas to the Optik Consultancy to see how far it conforms to our template. Finally, we will conceptualise a new canvas that can be replicated as a template for setting up similar programs in other disciplines. ACTUAL OR ANTICIPATED OUTCOMES By analysing the Optik Consultancy through the lens of an adapted Business Model Canvas, we will assess the key areas of our program from a different viewpoint. This will include justification of the program, the stakeholders involved, their needs and level of involvement, and the resources needed to make the program a success. Once this template has been established, we will have a conceptual tool that can be used to set up and analyse other WIL programs. CONCLUSIONS With some adaptations, the business model canvas can be applied to evaluate engineering WIL programs and provide a template to extend and review similar activities. To ensure that the model is applied accurately, further research will be necessary to evaluate the extent each area of the framework has been achieved