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Problem solving is a vital skill required to be successful in many engineering industries. One way for students to practice problem solving is through solving homework problems. However, solutions manuals for textbook problems are usually available online, and students can easily default to copying from solution manual. To address the solution manual dilemma and promote better problem-solving ability, this study utilizes novel homework problems that integrate a video component as an alternative to text-only, textbook problems. Building upon research showing visuals promote better learning, YouTube videos are reversed engineered by students to create new homework problems. Previous studies have catalogued student-written problems in a material and energy balance course, which are called YouTube problems. In this study, textbook homework problems were replaced with student-written YouTube problems. We additionally focused on examining learning attitudes after students solve YouTube problems. Data collection include attitudinal survey responses using a validated instrument called CLASS (Colorado Learning Attitudes about Science Survey). Students completed the survey at the beginning and end of the course. Analysis compared gains in attitudes for participants in the treatment groups. Mean overall attitude of participants undergoing YouTube intervention was improved by a normalized gain factor of 0.15 with a small effect size (Hedge’s g = 0.35). Improvement was most prominent in attitudes towards personal application and relation to real world connection with normalized gain of 0.49 and small effect size (Hedge’s g = 0.38). 

Problem solving is a signature skill of engineers. Here, problem solving is employed when students apply course concepts to reverse engineer YouTube videos and solve new student-written, homework-style problems (YouTube problems). Replacing textbook problems with YouTube problems, this research focuses on examining the rigor of YouTube problems as well as students’ problem-solving skills on textbook and YouTube problems. A quasi-experimental, treatment/control group design was employed, and data was collected and evaluated using multiple measurement instruments. First, rigor of homework problems was examined using the NASA Task Load Index. Also, problem solving was assessed using a previously-developed rubric called PROCESS: Problem definition, Representing the problem, Organizing the information, Calculations, Evaluating the solution, Solution communication, and Self-assessment. PROCESS was modified to independently measure completeness and accuracy of student responses, as well as identify errors committed in material and energy balances. In the treatment group, students were assigned ten textbook problems and nine YouTube problems. While the control group obtained higher PROCESS scores at the beginning of the study, both groups exhibited similar problem-solving skills near the end. Also, the rigor of student-written YouTube problems was similar to textbook problems related to the same course concepts. 

This evidence-based practices paper discusses the method employed in validating the use of a project modified version of the PROCESS tool (Grigg, Van Dyken, Benson, & Morkos, 2013) for measuring student problem solving skills. The PROCESS tool allows raters to score students’ ability in the domains of Problem definition, Representing the problem, Organizing information, Calculations, Evaluating the solution, Solution communication, and Self-assessment. Specifically, this research compares student performance on solving traditional textbook problems with novel, student-generated learning activities (i.e. reverse engineering videos in order to then create their own homework problem and solution). The use of student-generated learning activities to assess student problem solving skills has theoretical underpinning in Felder’s (1987) work of “creating creative engineers,” as well as the need to develop students’ abilities to transfer learning and solve problems in a variety of real world settings. In this study, four raters used the PROCESS tool to score the performance of 70 students randomly selected from two undergraduate chemical engineering cohorts at two Midwest universities. Students from both cohorts solved 12 traditional textbook style problems and students from the second cohort solved an additional nine student-generated video problems. Any large scale assessment where multiple raters use a rating tool requires the investigation of several aspects of validity. The many-facets Rasch measurement model (MFRM; Linacre, 1989) has the psychometric properties to determine if there are any characteristics other than “student problem solving skills” that influence the scores assigned, such as rater bias, problem difficulty, or student demographics. Before implementing the full rating plan, MFRM was used to examine how raters interacted with the six items on the modified PROCESS tool to score a random selection of 20 students’ performance in solving one problem. An external evaluator led “inter-rater reliability” meetings where raters deliberated rationale for their ratings and differences were resolved by recourse to Pretz, et al.’s (2003) problem-solving cycle that informed the development of the PROCESS tool. To test the new understandings of the PROCESS tool, raters were assigned to score one new problem from a different randomly selected group of six students. Those results were then analyzed in the same manner as before. This iterative process resulted in substantial increases in reliability, which can be attributed to increased confidence that raters were operating with common definitions of the items on the PROCESS tool and rating with consistent and comparable severity. This presentation will include examples of the student-generated problems and a discussion of common discrepancies and solutions to the raters’ initial use of the PROCESS tool. Findings as well as the adapted PROCESS tool used in this study can be useful to engineering educators and engineering education researchers.