Video Resources and Peer Collaboration in Engineering Mechanics: Impact and Usage Across Learning Outcomes

Video resources, largely in the form of recorded lectures or problem solutions, have become fairly commonplace in higher education classroom in the past few years. Video authoring tools and distribution channels are now powerful and seamless, presenting a wide array of new opportunities for faculty to produce sharable educational assets. Video resources, when created using pedagogical and multimedia best practices, are known to be valuable learning tools for students. A variety of studies have enlisted cognitive load theory and/or the worked example effect to demonstrate efficacy in a variety of settings and disciplines. In this paper, we examine the use of video resources by students in an undergraduate engineering mechanics (dynamics) class, with a specific focus on how video consumption correlates to the achievement of specific learning outcomes. We focus on video solutions to problems, and map student perceptions about the usefulness of the videos onto the learning outcomes for the course. Then, we map each graded assignment (homework, quiz, exam) onto those same learning outcomes, and compute an average score for each student on each learning outcome. We use student background information and data about total video consumption to further enrich the discussion. The results indicate that some students find video resources crucial to their academic success, across learning outcomes, while other students extract little value from the video resources. These students indicate that they prefer to work alone, with another technology (i.e., the textbook), or in study groups rather than engaging with the technology as a partner for learning. Some learning outcomes within the course, notably those related rigid body kinematics and rigid body kinetics (via Newton’s laws), reveal that students perceive high value of the videos regardless of their grade on assignments related to those outcomes. We find significant interplay with other factors reported on student background surveys, especially their views on collaboration. The data suggest that peer collaboration and video usage have a mutuallyreinforcing effect, with students actively engaged in both earning better grades in the course. Introduction Technology-based innovations in engineering education have a long history, and the relatively recent maturation of social media tools such as blogging and video have accelerated development of new approaches to support student learning. The idea of anywhere, anytime learning, supported by a variety of asynchronous resources, is particularly alluring in this modern era of hyperconnectivity. Giving students learning resources, and allowing them to choose when, where, how often, and with whom they use those resources holds the promise for powerful, personalized learning experiences. The sophistication of the learner matters, however, and it seems intuitive that some students are better at managing their academic environment than others. As such, there are many open questions about how students use technology resources for learning, especially in the context of their full academic workload and their general approach to learning. In this study, we have introduced video technologies within the context of a sophomore mechanics classroom, and we ask the specific questions: to what extent do the videos support P ge 26700.2 student learning? Does student usage of the videos vary with learning outcome? Are the videos better suited to enable learning for more complicated topics? And how do students weigh the relative value of learning with videos against the other options available to them (the textbook, their peers, and the instructor)? Review of relevant literature Learning by watching experts solve problems. Dynamics belongs to the class of foundational courses for mechanical, aerospace, and civil engineering students, and their mastery of these core concepts is crucial for future success in the curriculum as well as the workplace. Developing mastery often involves a combination of actually solving problems (live, on paper), as well as watching experts solve problems (via pre-recorded videos). Solving problems is both an intuitive and well-worn idea whose value is not disputed, and engineering students are constantly sharpening their problem solving skills by actually solving problems on homework assignments and exams. The other part of this dyad, watching experts solve problems, leverages the worked example effect (WE). In brief, WE contends that students can become better at many cognitive tasks by watching experts solve problems via carefully-constructed learning materials. Worked examples can be paper-based or video-based, and in general the literature converges on the idea that studying worked examples can form a powerful approach to learning. Worked example research has focused on all manner of technical topics, including secondary math education, electrical engineering, and even engineering mechanics and physics. Especially when extended with other pedagogical tools such as self-explanation prompts [8] or other kinds of scaffolding, worked examples are known to be a useful tool to support learning cognitively complex tasks with both efficiency and accuracy. Technology interventions and specific learning outcomes. Much of the worked example literature used a fairly controlled laboratory setting rather than an actual higher education classroom. Some of that literature focuses quite closely on mechanics related learning outcomes. Recent work using controlled eye gaze experiments examined how students learn physics concepts from worked examples, with the conclusions supporting the central tenets of both the worked example effect (via cognitive load theory) and effective multimedia design that leverages spatial contiguity principles. Quite a bit of work in similar laboratory settings has focused on quantifying specific aspects of physics or mechanics problem solving using eye gaze technologies and other instruments to evaluate student differences [13], . And again, the preponderance of the literature supports the idea that learning very specific topics can be effectively supported with a variety of worked examples and other technology-based interventions. Technology interventions in a classroom environment. Studies to evaluate interventions in a classroom environment are more difficult to execute for a variety of reasons, not the least of which is the issue of experimental controls. A recent meta-review of web 2.0 technologies concluded that at least the tools appear to do no harm in classrooms, and in the best cases they can be quite effective (such as when integrated into a full package of engaged pedagogies, assessments, and so forth that are self-consistent). Although the authors conclude that strong evidence is still lacking, they nonetheless argue that web 2.0 technologies can be effectively P ge 26700.3 deployed in a variety of classroom contexts. However, a counterpoint emerges when/if students perceive technology interventions to be “added” workload, an additional expectation, or in other ways conflicting with their preferred approach to learning. These two meta-reviews engage the underlying tensions of technology interventions in classroom environments: students perceive tremendous academic stresses on their time, and they make expeditious decisions about how to manage their workload; they seek to optimize task efficiency with task accuracy. To the extent that technology interventions clash with student expectations about how they best learn, such interventions may not be successful or even welcome. The gap in the literature. Taken together, these studies illustrate that technology-based interventions can be powerful aids to learning (the worked example effect), especially for cognitively complex tasks. This has been repeatedly shown in various laboratory environments across different technical subjects. Yet, when deployed in classroom environments, the interventions may expose underlying tensions about how students manage their workload within their educational ecosystem, and what instructional supports they are comfortable accessing. These individual student differences are important and can seriously impact their learning. We therefore observe a gap in the literature that helps to motivate this study: for students in a real classroom environment, what are the usage patterns of the various instructional supports (videos, peers, textbook, instructor) available to them, and in what ways does the ecosystem shape these usage patterns? This paper gives a preliminary look at these issues using data collected during a recent academic semester in a Dynamics course. Study population, data, and methodology Student population. The subjects in this study were students in the sophomore-level course Dynamics at a large, mid-Atlantic public university during the Spring 2012 semester. Total enrollment in the course was 120 students, drawn mostly from mechanical and aerospace engineering (about 85% of the total enrollment), but also including students from biomedical engineering and other disciplines. The course textbook was Hibbeler, and the Mastering platform was also used for online homework (HW) assignments (in addition to traditional handwritten homework assignments). There was a single section of this course offered in Spring 2012. Moreover, many of the students in this course were also enrolled in other single-section courses including Strength of Materials and a mathematics course (either differential equations, or probability and statistics). As such, there were dozens of students in this class who shared nearly-identical technical course schedules, and they therefore could easily form study groups for in-person collaboration. This question about collaboration habits is important and appears later in the paper. Course content and learning outcomes. This course followed the Hibbeler text in terms of presentation, with one nota