Creation and Implementation of the Innovation-Based Learning Framework: A Learning Analytics Approach

To meet the national and international call for creative and innovative engineers, many engineering departments and classrooms are striving to create more authentic learning spaces where students are actively engaging with design and innovation activities. For example, one model for teaching innovation is Innovation-Based Learning (IBL) where students learn fundamental engineering concepts and apply them to an innovation project with the goal of producing value outsidethe classroom. The model has been fairly successful, but questions still remain about how to best support students and instructors in open-ended innovation spaces. To answer these questions, learning analytics and educational data mining (LA/EDM) techniques were used to better understand student innovation in IBL settings. LA/EDM is a growing field with the goal of collecting and interpreting large amounts of educational data to support student learning. In this work, five LA/EDM algorithms and tools were developed: 1) the IBL framework which groups student actions into illustrative categories specific to innovation environments, 2) a classifier model that automatically groups student text into the categories of the framework, 3) classifier models that leverage the IBL framework to predict student success, 4) clustering models that group students with similar behavior, and 5) epistemic network analysis models that summarize temporal student behavior. For each of the five algorithms/tools, the design, development, assessment, and resulting implications are presented. Together, the results paint a picture of the affordances and challenges of teaching and learning innovation. The main insights gained are how language and temporal behavior provide meaningful information about students? learning and innovation processes, the unique challenges that result from incorporating open-ended innovation into the classroom, and the impact of using LA/EDM tools to overcome these challenges

Understanding Learners\u27 Motivation through Machine Learning Analysis on Reflection Writing

Educational data mining (EDM) is an emerging interdisciplinary field that utilizes a machine learning (ML) algorithm to collect and analyze educational data, aiming to better predict students\u27 performance and retention. In this WIP paper, we report our methodology and preliminary results from utilizing a ML program to assess students’ motivation through their upper-division years in the XYZ project-based learning (PBL) program. ML, or more specifically, the clustering algorithm, opens the door to processing large amounts of student-written artifacts, such as reflection journals, project reports, and written assignments, and then identifies keywords that signal their levels of motivation (i.e., extrinsic vs. intrinsic). These results will be compared against other measures of motivation, including student self-report, faculty observation, and externally validated surveys. As part of a longer-term study, this pilot work sheds light on the key question for student success and retention: how does student motivation evolve through the 3rd and 4th years in college? The purpose of this research project is to gain insights into learners’ motivation levels and how it evolves during the last two years in college, as well as to extend current Educational Data Mining research and Machine Learning analysis described in the literature. It is significant on two fronts: 1) we will extend the ability of ML in analyzing reflective written artifacts to explore student physiological and emotional development; 2) the longitudinal study will help monitor the progressive change of motivation in college students in a PBL environment. Preliminary results from an initial pilot study are promising. By analyzing written reflection journal entries from previous students, the ML algorithm has differentiated keywords into three student motivation levels: “high”, “neutral” and “low”. Using supervised classes, for example, the ML algorithm differentiated words in the highly motivated student text such as “team” and “learning”, while the text coded as low motivation included “use”, “pushed” and “nothing”. For our future research, we aim to create a dictionary that identifies words/phrases related to positive/negative motivation. We will extend the pilot study to a longitudinal evaluation of student motivation over four semesters of engineering education as well as prediction of student success in a PBL environment

Laying the Foundation for Education 4.0: Access, Value and Accountability

The complexity of the global problems engineers are working to solve has long been discussed in both engineering and engineering education circles. The Grand Challenges for Engineering are grand because of the complexity of the challenges. While the challenges stand over a decade later, the speed at which the terms in which they are described, the shift from Industry 3.0 to Industry 4.0, has been slow. As the world becomes more deeply connected, as the internet of things becomes more commonplace in all parts of our lives, as technologies like machine learning and cyber physical systems become accessible to even small businesses, the potential solutions to the current and future grand challenges change in ways we cannot yet predict and will require language to describe what we have not yet invented. Engineering education is living in a similar period of tumult. Many of the engineering tools and methods we have been relying on and teaching are of limited use in the Industry 4.0 and 5.0 worlds. Over the past few years, a sprinkling of scholarship has begun to define Engineering Education 4.0 in terms of teaching Industry 4.0 concepts and/or as pedagogical techniques such as video-based internet accessible instruction and collaborative virtual learning environments. This paper advances engineering education through laying out a a series of questions of what Engineering Education 4.0 means beyond a bundle of tools. This foundation includes the themes of access, value, and accountability. Access considers how Engineering Education 4.0 has the potential to increase equitable access to engineering education at all levels and varieties, including formal education, continuous lifelong learning, and informal learning within society. Value describes the benefits to the student, the learning environment (including the teacher), the institution, and society from the activities and results of engineering education. Value is generated through every course or set of micro-credentials in Engineering Education 4.0 and is explicitly articulated as part of the learning process. Accountability is the need at all units of analysis to demonstrate appropriate stewardship of resources to achieve the access and value promise of Engineering Education 4.0. Accountability is part of the credentialing process as well as part of the faculty and institutional evaluation systems. These three foundations will form the core of a paradigm that is intended to begin a scholarly dialogue to define Engineering Education 4.0

The Bell Academy: A Bridge Semester Where Engineering Students Transform Into Student Engineers Who Thrive In Industry Placements

Iron Range Engineering is an innovative learning program using project-based and work-based pedagogies. The Bell Academy (BA) is a semester-long bridge experience between the first two years of STEM foundation and the final two years spent in full-time industry co-op placements. The curriculum within the academy is delivered within three domains: technical, design, and professional. The transformation to thriving as a student engineer in an industry placement is intentionally embedded in each stage of the program as students develop higher levels of self-awareness, professional responsibility, and self-directedness. Students not only gain technical engineering knowledge, but also apply that knowledge within team-based, ill-structured design projects, acting as engineering consultants to industry clients. Technical learning is delivered in one-credit modules, which supports both the development of the individual as a student engineer and the execution of the project. Professional competencies are learned in-situ as teams encounter natural struggles. Development is supported through workshops, which cover topics such as conflict management, leadership, technical writing, data science, public speaking, inclusive action, etc. Through iterative assignments and practice, such as resume development, negotiation, and interviewing, students develop a skills portfolio to identify and acquire a position to begin and maintain their career. Through more than a decade of implementation, several unique learning strategies have been developed and refined. The paper will briefly describe the model used and provide the strategies as potential tools for adaptation and implementation in engineering programs worldwide

Using Classification and Clustering to Predict and Understand Student Behavior in an Innovation-Based Learning Course

One of the Grand Challenges for Engineering is advancing personalized learning, but challenges remain to identify and understand potential student pathways. This is especially difficult in complex, open-ended learning environments such as innovation-based learning courses. Student data from an iteration of an innovation-based learning course were analyzed using two educational data mining techniques: classification and clustering. Classification was used to predict student success in the course by creating a model that was both interpretable and robust (accuracy over 0.8 and ROC AUC of over 0.95). Clustering grouped student behavior into four main categories: Innovators, Learners, Surveyors, and Surface Level. Furthermore, noteworthy variables from each model were extracted to discover what factors were most likely to lead to course success. The work presented contributes to gaining a better understanding of how engineering students innovate and brings us closer to solving the Grand Challenge of advancing personalized learning

A far‑field radio‑frequency experimental exposure system with unrestrained mice

Radio-frequency (RF) energy is nearly everywhere, it is used in cell phones, wire-less internet and many other sources. These RF energy levels used by common devices are below the threshold level which does not produce heating of cells in living tissues. However, this low-level exposure of RF energy has still raised concerns over its possible effects on human health, specifically, genetic alterations. Researchers have investigated if RF energy can induce changes in biological function (Gherardini 2014; Kundi 2009; Polk and Postow 1995; Vanderstaeten and Verschaeve 2008). The methods used to investigate RF energy effects have varied widely depending on study. This variation in procedures has led to a lack of reproducibility, and because of that, inconclusive results (Gherardini 2014; Vanderstaeten and Verschaeve 2008). The goal of this paper is to describe a new experimental exposure system to explore the effects of far-field RF energy on biological function in unrestrained murine models, in vivo. Paffi et al. (2013) performed an extensive review of exposure systems. Many of these used a horn antenna to deliver RF energy, but lack long term continuous exposure for free moving murine models. Other studies including Kesari et al. (2010) and Wasoonta