Conference Session

Technical Session 6: Modulus Topics Part 2

Collection

2019 ASEE Annual Conference & Exposition

Authors

Paras Sud, University of Illinois, Urbana-Champaign; Matthew West, University of Illinois, Urbana-Champaign; Craig Zilles, University of Illinois, Urbana-Champaign

Tagged Divisions

Computers in Education

: function MEAN EXAM SCORE BY QUINTILE(exam, quintile) 2: points = 0 3: max points = 0 4: for q in get questions(exam) do 5: mean = question score by quintile(q, quintile) 6: points = points + mean 7: max points = max points + get max points(q) 8: end for 9: return points/max points10: end functionWe then define the unfairness of a collection of exams for a given quintile as the standarddeviation of the expected scores for that quintile across all of the exams.To be clear, a collection of exams is not necessarily unfair if there is high variance in the studentscores when students are given different exams from this collection. We expect such a variance inscore resulting from a variance in student abilities. We

Conference Session

Technical Session 4: Modulus Topics 1

Collection

2019 ASEE Annual Conference & Exposition

Authors

Yamuna Rajasekhar, zyBooks; Alex Daniel Edgcomb, Zybooks; Frank Vahid, University of California, Riverside

Tagged Divisions

Computers in Education

and rThis topic introduces students to sequential circuits, typically covered during the first half of thesemester. An SR latch is the simplest circuit that stores 1-bit. A timing diagram is a commonway to analyze the inputs and outputs of such a circuit. The objective of this activity is tofamiliarize the students with the workings of an SR latch. This is done with a timing diagram asin Figure 2. This activity has two levels of progression with equal difficulty. Each level presentsa randomly-generated combinations of s and r, and the student needs to input the corresponding qfor each combination of s and r, as in Figure 2(a). Clicking a square in q toggles between 1 and 0values. When a student submits, the activity compares the student's q

Conference Session

Technical Session 7: Online and Distributed Learning

Collection

2019 ASEE Annual Conference & Exposition

Authors

Taylor V. Williams, Purdue University-Main Campus, West Lafayette (College of Engineering); Kerrie A. Douglas, Purdue University-Main Campus, West Lafayette (College of Engineering); Peter Bermel, Purdue University-Main Campus, West Lafayette (College of Engineering); Hillary E. Merzdorf, Purdue University-Main Campus, West Lafayette (College of Engineering)

Tagged Divisions

Computers in Education

.[11] R. Deng, P. Benckendorff, and D. Gannaway, “Progress and new directions for teaching and learning in MOOCs,” Computers & Education, vol. 129, pp. 48-60, 2019.[12] Q. Li, Q and R. Baker, “The different relationships between engagement and outcomes across participant subgroups in Massive Open Online Courses,” Computers & Education, vol. 127, pp. 41-65, 2018.[13] C. C. Gray and G. Perkins, “Utilizing early engagement and machine learning to predict student outcomes,” Computers & Education, vol. 131, pp. 22-32, 2019.[14] H. Qu and Q. Chen, “Visual analytics for MOOC data,” IEEE Computer Graphics and Applications, vol. 6, pp. 69-75, 2015.[15] A. F. Wise, “Designing pedagogical interventions to support student

Conference Session

Technical Session 1: Issues Impacting Students Learning How to Program

Collection

2019 ASEE Annual Conference & Exposition

Authors

J.w. Bruce, Tennessee Technological University; Bryan A. Jones, Mississippi State University; Mahnas Jean Mohammadi-Aragh, Mississippi State University

Tagged Divisions

Computers in Education

. Survey Statement ChoicesQ1 I would rate my knowledge of HDL and digital Poor (0), Fair (1), Satisfactory systems design knowledge at the start of the course (2), Very Good (3), Excellent (4)Q2 I would rate my knowledge of HDL and digital Poor (0), Fair (1), Satisfactory systems design knowledge at the conclusion of the (2), Very Good (3), Excellent (4) courseQ3 Writing detailed and descriptive comments in my HDL Strongly disagree (-2), Disagree descriptions helped me to learn. (1), Neither disagree or agree (0), Agree (+1), Strongly agree (+2)Q4 I would have rather had traditional Q

Conference Session

Poster Session

Collection

2019 ASEE Annual Conference & Exposition

Authors

Kenie R. Moses

Tagged Divisions

Computers in Education

Affective Tutoring System,”Workshop on Motivational and Affective Issues in ITS, 8th International Conference on ITS 2006, pp. 38-45, 2006.[41] M. A. Ringenberg and K. VanLehn, “Scaffolding problem solving with annotated, worked-outexamples to promote deep learning,” in Intelligent tutoring systems, pp. 625–634, 2006.[42] M. Alves, C. S. Rodrigues, A. M. A. C. Rocha, and C. Coutinho, “Self-efficacy, mathematics’ anxietyand perceived importance: an empirical study with Portuguese engineering students,” European Journal ofEngineering Education, vol. 41, no. 1, pp. 105–121, Jan. 2016.[43] Q. Brown, “Mobile intelligent tutoring system: moving intelligent tutoring systems off the desktop,”PhD thesis, Drexel University, 2009.

Conference Session

Technical Session 11: Topics related to Computer Science

Collection

2019 ASEE Annual Conference & Exposition

Authors

Farzana Rahman, Florida International University; Samy El-Tawab, James Madison University

Tagged Divisions

Computers in Education

Conference Session

Technical Session 5: Topics related to Engineering

Collection

2019 ASEE Annual Conference & Exposition

Authors

Paul Morrow Nissenson, California State Polytechnic University, Pomona; Nolan Tsuchiya P.E., California State Polytechnic University, Pomona; Mariappan Jawaharlal, California State Polytechnic University, Pomona; Angela C. Shih, California State Polytechnic University, Pomona

Tagged Divisions

Computers in Education

, Columbus, OH, USA, June 25-28,2017.[19] F. L. Wachs, J. L. Fuqua, P. M. Nissenson, A. C. Shih, M. P. Ramirez, L. Q. DaSilva, N. Nguyen, and C.Romero, “Successfully flipping a fluid mechanics course using video tutorials and active learning strategies:Implementation and Assessment,” in Proceedings of the 2018 American Society for Engineering Education AnnualConference & Exposition, Salt Lake City, UT, USA, June 24-27, 2018.[20] P. M. Nissenson, “Impact of a hybrid format on student performance and perceptions in an introductorycomputer programming course,” in Proceedings of the 2015 American Society for Engineering Education PacificSouthwest Section Conference, San Diego, CA, April 9-11, 2015.[21] P. M. Nissenson, “Impact of varying in-class

Conference Session

Technical Session 11: Topics related to Computer Science

Collection

2019 ASEE Annual Conference & Exposition

Authors

Leila Zahedi, Florida International University; Monique S Ross, Florida International University; Jasmine Skye Batten, Florida International University

Tagged Topics

Diversity

Tagged Divisions

Computers in Education

can enhance students’engagement and develop their process of learning. However, there is a lack of well-organizedguidelines (Azmi et al., 2015).In one study, Ibanez et al. (2014) developed a Q-learning game platform to investigate the effectsof gamification on a learning activity targeted at basic concepts of C programming language toundergraduate students. According to the mixed-methods study, gamified learning activities had asignificant positive impact on the students’ engagement and improved their academicperformance. Game elements such as badges, points, leaderboard, and altruism were inserted intothis game platform. Students reported that points were the most motivating element to participatein activities. However, the authors indicated