Early prediction of students at risk of doing poorly in CS1 can enable early interventions or class adjustments. Preferably, prediction methods would be lightweight, not requiring much extra activity or data-collection work from instructors beyond what they already do. Previous methods included giving surveys, collecting (potentially sensitive) demographic data, introducing clicker questions into lectures, or using locally-developed systems that analyze programming behavior, each requiring some effort by instructors. Today, a widely used textbook / learning system in CS1 classes is zyBooks, used by several hundred thousand students annually. The system automatically collects data related to reading, homework, and programming assignments. For a 300+ student CS1 class, we found that three data metrics, auto-collected by that system in early weeks (1-4), were good at predicting performance on the week-6 midterm exam: non-earnest completion of the assigned readings, struggle on the coding homework, and low scores on the programming assignments, with correlation magnitudes of 0.44, 0.58, and 0.72, respectively. We combined those metrics in a decision tree model to predict students at-risk of failing the midterm exam (<70%, meaning D or F), and achieved 85% prediction accuracy with 82% sensitivity and 89% specificity, which is higher than previously-published early-prediction approaches. The approach may mean that thousands of instructors already using zyBooks (or a similar system) can get a more accurate early prediction of at-risk students, without requiring extra effort or activities, and avoiding collection of sensitive demographic data.

Cheating in introductory programming classes (CS1) is a well-known problem. Various methods have been suggested to reduce cheating, but many are time-consuming, resource intensive, or don't scale to large classes. We introduced a class intervention having 6 low-effort commonly-suggested methods to reduce cheating: (1) Discussing academic integrity for 20-30 minutes, several weeks into the term, (2) Requiring an integrity quiz with explicit do's and don'ts, (3) Allowing students to retract program submissions, (4) Reminding students mid-term about integrity and consequences of getting caught, (5) Showing instructor tools in class (including a similarity checker, statistics on time spent, and access to a student's full coding history), (6) Normalizing help and pointing students to help resources. Via manual evaluation of similarity checker results on 7 held-constant labs with one instructor teaching 100-student sections, for two pre-intervention and two intervention sections, suspected-cheating reduced 62% (30.5% down to 11.5%). Because manual evaluation could be biased and is time consuming, we developed two automated coding-behavior metrics per lab -- time spent programming, and % of students with highly-similar code -- that may suggest how much cheating is happening. Time spent increased by 56% (7 min to 10.9 min), and % of students with highly-similar code dropped 48% (38.5% to 20%). We later repeated the intervention with a second instructor and different labs and achieved similar (in fact, even stronger) results, with time rising 84% (13 min to 24 minutes) and % dropping 66% (55.5% to 19%). All findings were statistically significant with p < 0.0001.

College textbook publishing is transforming from a model of static textbooks to a modern model of customizable textbooks. Customization may involve reconfiguring content, combining textbooks, authoring one's own content, adding notes to content, and more. As such, publishing is moving away from a model of selling static textbooks, and toward a model of providing a library of content from which instructors can build a course. This Full Paper provides data for one digital-only publisher, zyBooks, on the prevalence and trends around reconfiguring and combining Computer Science and Engineering textbooks, instructor-authored sections, and instructor-added notes. The data show that for over 4,000 classes in 2020, over 85% of classes reconfigured their books, over 30% of classes combined two or more books with hundreds combining three or more, about 30% of books had instructor notes added, and about 65% of zyLabs-enabled zyBooks included instructor-created labs. The trend away from static textbooks and toward customizable content has substantial implications on how content is authored, requiring more modularity of content sections to support reconfiguration, and requiring more consistency across subjects to enable combining content. The trend also has substantial implications on book marketing, pricing, renewals, and more.