Quantifying Incremental Development Practices and Their Relationship to Procrastination

Kazerouni, Ayaan M.; Edwards, Stephen H.; Shaffer, Clifford A.

doi:10.1145/3105726.3106180

Cited by 30 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is likely a problem specific to a human behaviour. One of the possible explanation is procrastination, a phenomenon of preferring short-term goal over the long-term goals and then postponing the activity until the very end [20,30].…”

Section: Discussionmentioning

confidence: 99%

Are we meeting a deadline? classification goal achievement in time in the presence of imbalanced data

Hlosta

Zdráhal

Zendulka

2018

Knowledge-Based Systems

View full text Add to dashboard Cite

This paper presents the problem where objects out of a finite set are required to achieve a goal within a predefined deadline. For example, a group of students is supposed to submit a homework by a specified cutoff. Further, we are interested in predicting, which objects will achieve the goal within the deadline. The predictive models are built only based on the data from that population. The predictions are computed in various time instants by taking into account updated data about the objects. The first contribution of the paper is a formal description of the problem. The important characteristic of the proposed method for model building is the use of the properties of objects that have already achieved the goal. We call such approach Self-Learning. Since only a few of objects have achieved the goal at the beginning and their number gradually grows, the problem is inherently imbalanced. To mitigate the curse of imbalance, we improved the Self-Learning method by tackling the information loss and by several sampling techniques. The original Self-Learning and the modifications have been evaluated in the case study for predicting submission of the first assessment in the distance higher education courses. The results show that the proposed improvements outperforms the specified two base-line models, the original Self-Learner and also that the best results are achieved if domain-driven techniques are utilised to tackle the imbalance problem.

show abstract

Section: Discussionmentioning

confidence: 99%

Are we meeting a deadline? classification goal achievement in time in the presence of imbalanced data

Hlosta

Zdráhal

Zendulka

2018

Knowledge-Based Systems

View full text Add to dashboard Cite

show abstract

“…The research of Kazerouni and colleagues explains that the assessment of learner habits, such as additional growth and procrastination, has a direct relationship with the accuracy of solutions, the completion time of the given work, or the overall amount of time spent working on a solution. They also found that procrastination contributes to lower scores [24].…”

Section: Introductionmentioning

confidence: 94%

“…A standard method of calculating procrastination is to measure the total time a student is engaging with or communicating with the LMS before a course term. Additionally, studies that take these kinds of measures as a sign of procrastination show that the symbols have a negative association with course results [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers

et al. 2020

View full text Add to dashboard Cite

Ubiquitous online learning is continuing to expand, and the factors affecting success and educational sustainability need to be quantified. Procrastination is one of the compelling characteristics that students observe as a failure to achieve the weaker outcomes. Past studies have mainly assessed the behaviors of procrastination by describing explanatory work. Throughout this research, we concentrate on predictive measures to identify and forecast procrastinator students by using ensemble machine learning models (i.e., Logistic Regression, Decision Tree, Gradient Boosting, and Forest). Our results indicate that the Gradient Boosting autotuned is a predictive champion model of high precision compared to the other default and hyper-parameterized tuned models in the pipeline. The accuracy we enumerated for the VALIDATION partition dataset is 91.77 percent, based on the Kolmogorov–Smirnov statistics. Additionally, our model allows teachers to monitor each procrastinator student who interacts with the web-based e-learning platform and take corrective action on the next day of the class. The earlier prediction of such procrastination behaviors would assist teachers in classifying students before completing the task, homework, or mastery of a skill, which is useful and a path to developing a sustainable atmosphere for education or education for sustainable development.

show abstract

“…Following Kazerouni et al [27], we split the event stream for a subject working on a project into work sessions. Work sessions are sequences of activity, and are delimited by an hour or more of inactivity.…”

Section: Work Sessionsmentioning

confidence: 99%

Assessing Incremental Testing Practices and Their Impact on Project Outcomes

Kazerouni

Shaffer

Edwards

et al. 2019

Proceedings of the 50th ACM Technical Symposium on Computer Science Education

Self Cite

View full text Add to dashboard Cite

Software testing is an important aspect of the development process, one that has proven to be a challenge to formally introduce into the typical undergraduate CS curriculum. Unfortunately, existing assessment of testing in student software projects tends to focus on evaluation of metrics like code coverage over the finished software product, thus eliminating the possibility of giving students early feedback as they work on the project. Furthermore, assessing and teaching the process of writing and executing software tests is also important, as shown by the multiple variants proposed and disseminated by the software engineering community, e.g., test-driven development (TDD) or incremental test-last (ITL). We present a family of novel metrics for assessment of testing practices for increments of software development work, thus allowing early feedback before the software project is finished. Our metrics measure the balance and sequence of effort spent writing software tests in a work increment. We performed an empirical study using our metrics to evaluate the test-writing practices of 157 advanced undergraduate students, and their relationships with project outcomes over multiple projects for a whole semester. We found that projects where more testing effort was spent per work session tended to be more semantically correct and have higher code coverage. The percentage of method-specific testing effort spent before production code did not contribute to semantic correctness, and had a negative relationship with code coverage. These novel metrics will enable educators to give students early, incremental feedback about their testing practices as they work on their software projects.

show abstract

Quantifying Incremental Development Practices and Their Relationship to Procrastination

Cited by 30 publications

References 14 publications

Are we meeting a deadline? classification goal achievement in time in the presence of imbalanced data

Are we meeting a deadline? classification goal achievement in time in the presence of imbalanced data

Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers

Assessing Incremental Testing Practices and Their Impact on Project Outcomes

Contact Info

Product

Resources

About