Using and Collecting Fine-Grained Usage Data to Improve Online Learning Materials

Leppänen, Leo; Leinonen, Juho; Ihantola, Petri; Hellas, Arto

doi:10.1109/icse-seet.2017.12

Cited by 12 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The course components were long HTML pages; each page can be considered an analogue to a chapter of a traditional textbook. The online ebook used a JavaScript library that stores information on the use of the materials [22].…”

Section: Onlinementioning

confidence: 99%

Comparison of Time Metrics in Programming

Leinonen

Leppänen

Ihantola

et al. 2017

Proceedings of the 2017 ACM Conference on International Computing Education Research

Self Cite

View full text Add to dashboard Cite

Research on the indicators of student performance in introductory programming courses has traditionally focused on individual metrics and specific behaviors. These metrics include the amount of time and the quantity of steps such as code compilations, the number of completed assignments, and metrics that one cannot acquire from a programming environment. However, the differences in the predictive powers of different metrics and the cross-metric correlations are unclear, and thus there is no generally preferred metric of choice for examining time on task or effort in programming.In this work, we contribute to the stream of research on student time on task indicators through the analysis of a multi-source dataset that contains information about students' use of a programming environment, their use of the learning material as well as self-reported data on the amount of time that the students invested in the course and per-assignment perceptions on workload, educational value and difficulty. We compare and contrast metrics from the dataset with course performance. Our results indicate that traditionally used metrics from the same data source tend to form clusters that are highly correlated with each other, but correlate poorly with metrics from other data sources. Thus, researchers should utilize multiple data sources to gain a more accurate picture of students' learning.

show abstract

Section: Onlinementioning

confidence: 99%

Comparison of Time Metrics in Programming

Leinonen

Leppänen

Ihantola

et al. 2017

Proceedings of the 2017 ACM Conference on International Computing Education Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…This might occur either actively or passively as they take part in a course. Such data has been used, for example, to study students' learning and behaviour [10], to identify at-risk students using predictive models [10], and to improve educational materials [12].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Students' Peer Reviews to Crowdsourced Programming Assignments

Pirttinen

Kangas

Nygren

et al. 2018

Proceedings of the 18th Koli Calling International Conference on Computing Education Research

Self Cite

View full text Add to dashboard Cite

We have used a tool called CrowdSorcerer that allows students to create programming assignments. The students are given a topic by a teacher, after which the students design a programming assignment: the assignment description, the code template, a model solution and a set of input-output -tests. The created assignments are peer reviewed by other students on the course. We study students' peer reviews to these student-generated assignments, focusing on examining the differences between novice and experienced programmers. We then analyze whether the exercises created by experienced programmers are rated better quality-wise than those created by novices. Additionally, we investigate the differences between novices and experienced programmers as peer reviewers: can novices review assignments as well as experienced programmers? CCS CONCEPTS• Information systems → Crowdsourcing; • Social and professional topics → Computing education;

show abstract

“…in the affirmative. These results, together with results in [27], indicate that researchers should pay attention to what the students are reading instead of coarse grained movement data.…”

Section: Materials Usage Predicts Successmentioning

confidence: 64%

“…Previous works have also extracted small amounts of higher level features from online learning management systems and predicted learning outcomes from said features [27,31,39].…”

Section: Related Workmentioning

confidence: 99%

Predicting Academic Success Based on Learning Material Usage

Leppänen

Leinonen

Ihantola

et al. 2017

Proceedings of the 18th Annual Conference on Information Technology Education

Self Cite

View full text Add to dashboard Cite

In this work, we explore students' usage of online learning material as a predictor of academic success. In the context of an introductory programming course, we recorded the amount of time that each element such as a text paragraph or an image was visible on the students' screen. Then, we applied machine learning methods to study to what extent material usage predicts course outcomes. Our results show that the time spent with each paragraph of the online learning material is a moderate predictor of student success even when corrected for student time-on-task, and that the information can be used to identify at-risk students. The predictive performance of the models is dependent on the quantity of data, and the predictions become more accurate as the course progresses. In a broader context, our results indicate that course material usage can be used to predict academic success, and that such data can be collected in-situ with minimal interference to the students' learning process.

show abstract

Using and Collecting Fine-Grained Usage Data to Improve Online Learning Materials

Cited by 12 publications

References 24 publications

Comparison of Time Metrics in Programming

Comparison of Time Metrics in Programming

Analysis of Students' Peer Reviews to Crowdsourced Programming Assignments

Predicting Academic Success Based on Learning Material Usage

Contact Info

Product

Resources

About