Doctoral writing workshops: A pre-registered, randomized controlled trial [Scholarly Article - Innovative Higher Education, 2022]

Title:

Doctoral writing workshops: A pre-registered, randomized controlled trial

 

Authors:

Barbara W. Sarnecka, Paulina N. Silva, Jeff Coon, Darby C. Vickers, Rena B. Goldstein & Jeffrey N. Rouder 

 

Published:

Innovative Higher Education, 47, 155-174 (19 January 2022)

https://link.springer.com/article/10.1007/s10755-021-09574-6

Abstract:

Doctoral students were randomly assigned to a five-week (30-h) faculty-led writing workshop intervention, either preceded by a five-week (waiting list) control phase or followed by a five-week maintenance phase. In the workshop, students wrote together, received instruction in genres of academic writing (literature reviews, scientific articles, funding proposals, and presentations), and exchanged feedback on drafts. As a result of the workshop students enjoyed writing more, found writing easier, and gained confidence in themselves as academic writers. They felt able to write productively in shorter blocks of time, and they engaged in more short-term, medium-term, and long-term planning of their research. The intervention also caused participants to pause more frequently for reflection or positive thinking and to generate more new writing. Effects were maintained in a peer-led writing maintenance group for at least five weeks after the intervention ended. This is the first randomized controlled trial of a doctoral-level writing intervention to date and has the potential to support doctoral training in academic and scientific writing across the Social Sciences, Education, and the Humanities.

 

Scholarly Article (April 2020) - The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

Title:
The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

Authors:
Rachel Baker, Di Xu, Jihyun Park, Renzhe Yu, Qiujie Li, Bianca Cung, Christian Fischer, Fernando Rodriguez, Mark Warschauer & Padhraic Smyth

Published:
International Journal of Educational Technology in Higher Education, 17(13), 2020
https://doi.org/10.1186/s41239-020-00187-1

From the abstract:
"Student clickstream data—time-stamped records of click events in online courses—can provide fine-grained information about student learning. Such data enable researchers and instructors to collect information at scale about how each student navigates through and interacts with online education resources, potentially enabling objective and rich insight into the learning experience beyond self-reports and intermittent assessments. Yet, analyses of these data often require advanced analytic techniques, as they only provide a partial and noisy record of students’ actions. Consequently, these data are not always accessible or useful for course instructors and administrators. In this paper, we provide an overview of the use of clickstream data to define and identify behavioral patterns that are related to student learning outcomes."