Engineering education aims to create a learning environment capable of developing vital engineering skill sets, preparing students to enter the workforce and succeed as future leaders. With all the rapid technological advancements, new engineering challenges continuously emerge, impeding the development of engineering skills. This insufficiency in developing the required skills resulted in high regression rates in students’ GPAs, resulting in industries reporting graduates’ unsatisfactory performance. From a pedagogical perspective, this problem is highly correlated with traditional learning methods that are inadequate for engaging students and improving their learning experience when adopted alone. Accordingly, educators have incorporated new learning methodologies to address the pre-defined problem and enhance the students’ learning experience. However, many of the currently adopted teaching methods still lack the potential to expose students to practical examples, and they are inefficient among engineering students, who tend to be active learners and prefer to use a variety of senses. To address this, our research team proposes integrating the technology of virtual reality (VR) into the laboratory work of engineering technology courses to improve the students’ learning experience and engagement. VR technology, an immersive high-tech media, was adopted to develop an interactive teaching module on hydraulic gripper designs in a VR construction-like environment. The module aims to expose engineering technology students to real-life applications by providing a more visceral experience than screen-based media through the generation of fully computer-simulated environments in which everything is digitized. This work presents the development and implementation of the VR construction lab module and the corresponding gripper designs. The virtual gripper models are developed using Oculus Virtual Reality (OVR) Metrics Tool for Unity, a Steam VR Overlay utility created to make visualizing the desktop in a VR setting simple and intuitive. The execution of the module comprises building the VR environment, designing and importing the gripper models, and creating a user-interface VR environment to visualize and interact with the model (gripper assembly/mechanism testing). Besides the visualization, manipulation, and interaction, the developed VR system allows for additional features like displaying technical information, guiding students throughout the assembly process, and other specialized options. Thus, the developed interactive VR module will serve as a perpetual mutable platform that can be readily adjusted to allow future add-ons to address future educational opportunities.
more »
« less
Victims of Outcomes: Towards an Enactivist Model of Technological Literacy
Cyclical models are often used to describe how students learn and develop. These models usually focus on the cognitive domain and describe how knowledge and skills are learned within a course or classroom. By providing insights into how students learn and thus how an instructor can support learning, these models and the schemas drawn from them also influence beliefs about learning and thus how educational programs are designed and developed. In this paper the authors present an alternative cyclical model of learning that is drawn from a philosophy of enactivism rather than rational dualism. In comparison with the dualism inherent in viewpoints derived from Descartes where learners construct internal mental representation from inputs received from the external world, in enactivism development occurs through continual dynamic interactions between an agent and their environment. Enactivism thus emphasizes the role environments play in learning and development.
The model developed in this paper hypothesizes that the environment in which learning typically occurs can be represented by three elements: the learner’s identity and culture which informs personally significant goals and values; the affordances a degree program offers in areas of knowledge, identity, and context which informs the capabilities of the environment; and the implicit and explicit goals of education as they are negotiated and understood by learners and teachers. These three elements are strongly coupled and together define the ever-changing learning environment.
The paper explores how changing technologies and cultures affect each of these three elements in regards to students’ ability to become technologically literate. While rational or dualist views of education see such environmental changes as peripheral to developing accurate representations of truth, enactivism posits that environment significantly affects the process of education. Because each student or faculty member is a participant in a learning organization changes within the organization—whether externally or internally driven—change the learning process. If education is deemed successful when students can transfer learning to new contexts, dualist models assume transfer is weakly coupled to educational environments while the enactivist viewpoint posits that environments strongly affect transfer.
The enactivist model can inform efforts to encourage technological literacy. Like many areas in STEM, education technological literacy has sought to identify and support learning outcomes that specify effective teaching or content interventions which enable learners to become more technologically literate. From the enactivist perspective, however, technological literacy is achieved by placing individuals into an environment in which they must navigate technology-induced challenges, with success defined as learning processes that allow learners to manage tensions inherent in their environment. Because most students already live in such environments teaching definable or enumerable outcomes makes less sense than helping student to be metacognitive and reflective how they manage and relate with technology.
more »
« less
- Award ID(s):
- 2022271
- NSF-PAR ID:
- 10356575
- Date Published:
- Journal Name:
- American Society for Engineering Education Annual Conference and Exhibition
- Page Range / eLocation ID:
- #37854
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract Machine learning (ML) provides a powerful framework for the analysis of high‐dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non‐linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows, as larger and more complex datasets become available through massive open online courses (MOOCs) and large‐scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. To provide educational researchers with an elaborate introduction to the topic, we provide an instructional summary of the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. Specifically, we (1) provide an overview of the types of data suitable for ML and (2) give practical advice for the application of ML methods. In each section, we provide analytical examples and reproducible R code. Also, we provide an extensive Appendix on ML‐based applications for education. This instructional summary will help educational scientists and practitioners to prepare for the promises and threats that come with the shift towards digitisation and large‐scale assessment in education.
Context and implications Rationale for this study In 2020, the worldwide SARS‐COV‐2 pandemic forced the educational sciences to perform a rapid paradigm shift with classrooms going online around the world—a hardly novel but now strongly catalysed development. In the context of data‐driven education, this paper demonstrates that the widespread adoption of machine learning techniques is central for the educational sciences and shows how these methods will become crucial tools in the collection and analysis of data and in concrete educational applications. Helping to leverage the opportunities and to avoid the common pitfalls of machine learning, this paper provides educators with the theoretical, conceptual and practical essentials.
Why the new findings matter The process of teaching and learning is complex, multifaceted and dynamic. This paper contributes a seminal resource to highlight the digitisation of the educational sciences by demonstrating how new machine learning methods can be effectively and reliably used in research, education and practical application.
Implications for educational researchers and policy makers The progressing digitisation of societies around the globe and the impact of the SARS‐COV‐2 pandemic have highlighted the vulnerabilities and shortcomings of educational systems. These developments have shown the necessity to provide effective educational processes that can support sometimes overwhelmed teachers to digitally impart knowledge on the plan of many governments and policy makers. Educational scientists, corporate partners and stakeholders can make use of machine learning techniques to develop advanced, scalable educational processes that account for individual needs of learners and that can complement and support existing learning infrastructure. The proper use of machine learning methods can contribute essential applications to the educational sciences, such as (semi‐)automated assessments, algorithmic‐grading, personalised feedback and adaptive learning approaches. However, these promises are strongly tied to an at least basic understanding of the concepts of machine learning and a degree of data literacy, which has to become the standard in education and the educational sciences.
Demonstrating both the promises and the challenges that are inherent to the collection and the analysis of large educational data with machine learning, this paper covers the essential topics that their application requires and provides easy‐to‐follow resources and code to facilitate the process of adoption.
-
The authors present the design and implementation of an exploratory virtual learning environment that assists children with autism (ASD) in learning science, technology, engineering, and mathematics (STEM) skills along with improving social-emotional and communication skills. The primary contribution of this exploratory research is how educational research informs technological advances in triggering a virtual AI companion (AIC) for children in need of social-emotional and communication skills development. The AIC adapts to students’ varying levels of needed support. This project began by using puppetry control (human-in-the-loop) of the AIC, assisting students with ASD in learning basic coding, practicing their social skills with the AIC, and attaining emotional recognition and regulation skills for effective communication and learning. The student is given the challenge to program a robot, Dash™, to move in a square. Based on observed behaviors, the puppeteer controls the virtual agent’s actions to support the student in coding the robot. The virtual agent’s actions that inform the development of the AIC include speech, facial expressions, gestures, respiration, and heart color changes coded to indicate emotional state. The paper provides exploratory findings of the first 2 years of this 5-year scaling-up research study. The outcomes discussed align with a common approach of research design used for students with disabilities, called single case study research. This type of design does not involve random control trial research; instead, the student acts as her or his own control subject. Students with ASD have substantial individual differences in their social skill deficits, behaviors, communications, and learning needs, which vary greatly from the norm and from other individuals identified with this disability. Therefore, findings are reported as changes within subjects instead of across subjects. While these exploratory observations serve as a basis for longer term research on a larger population, this paper focuses less on student learning and more on evolving technology in AIC and supporting students with ASD in STEM environments.more » « less
-
Technological developments have spawned a range of educational software that strives to enhance learning through personalized adaptation. The success of these systems depends on how accurate the knowledge state of individual learners is modeled over time. Computer scientists have been at the forefront of development for these kinds of distributed learning systems and have primarily relied on data-driven algorithms to trace knowledge acquisition in noisy and complex learning domains. Meanwhile, research psychologists have primarily relied on data collected in controlled laboratory settings to develop and validate theory-driven computational models, but have not devoted much exploration to learning in naturalistic environments. The two fields have largely operated in parallel despite considerable overlap in goals. We argue that mutual benefits would result from identifying and implementing more accurate methods to model the temporal dynamics of learning and forgetting for individual learners. Here we discuss recent efforts in developing adaptive learning technologies to highlight the strengths and weaknesses inherent in the typical approaches of both fields. We argue that a closer collaboration between the educational machine learning/data mining and cognitive psychology communities would be a productive and exciting direction for adaptive learning system application to move in.more » « less
-
There is a critical need for research-based active learning instructional materials for the teaching and learning of STEM in online courses. Every year, hundreds of thousands of undergraduate non-science majors enroll in general education astronomy courses to fulfill their institution’s liberal arts requirements. When designing instructional materials for this population of learners, a central focus must be to help learners become more scientifically and data literate. As such, we developed a new, three-part, curricular model that was used to inform the creation of active-learning instructional materials designed for use in online courses. The instructional materials were designed to help introductory astronomy students engage meaningfully with science while simultaneously improving their data literacy self-efficacy (especially as it pertained to making evidence-based conclusions when presented with a variety of data representations). We conducted a pilot study of these instructional materials at nine different colleges and universities to better understand whether students’ engagement with these materials lead to improved beliefs and self-efficacy. The results of our student survey analysis showed statistically significant changes on survey items that assessed students’ beliefs about science engagement, citizen science, and their data literacy skills. Additionally, we assessed whether faculty who implemented these materials were able to easily incorporate them into existing online astronomy courses. The instructor feedback emphasized that our curriculum development model did successfully inform the creation of easy-to-implement instructional materials, generating the potential for widespread dissemination and use at the undergraduate level.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
