Researchers: Colin B. Pinnell, Jason Bell, Moushir M. El-Bishouty, Lanqin Zheng
SCRL stands for Self-/Co- Regulated Learning.
SCRL aims at measuring and improving students’ self-regulation and co-regulation abilities.
Introductory Note by Vive Kumar
by Moushir M. El-Bishouty
It aims at supporting students’ self-regulation and co-regulation in order to foster their computational competency. SCRL tool implements Winne and Hadwin’s model of self-regulated learning that influenced by information-processing theory (IPT). In this model, learning occurs in four basic phases (task definition, goal setting and planning; studying tactics; and adaptations to metacognition. Moreover, the tool monitors learners’ competency through analyzing their performance and interaction data captured by a set of software sensors that measure competency development. The current phase of the project focuses on two application domains: programming and writing competencies. The developed sensors capture students’ interactions while using leaning platforms, such as learning management system (LMS) and integrated development environment (IDE). The captured data conform the raw data that are utilized by SCRL for monitoring, measuring and evaluation students’ regulated learning and competency development.
SCRL is a large, ambitious project, and as such we are developing it in several stages. This gives us two major benefits: firstly, it allows us to develop in chunks. Given that we’re a smaller team, we need to be able to break things apart into smaller pieces in order to get meaningful work done.
More importantly, though, many of our ideas are untested and need verification. There’s no sense implementing a large system if parts of it simply aren’t true in the field! Developing in stages lets us test individual parts of our hypotheses before joining them into the full system.
SCRL 1.0 – Deployed, Tested, In Service
SCRL 1.0 was designed to service two goals – to prove the concept that a self-regulation tool could present an analytics team with useful data, and to provide us with a repository of practical data regarding student regulatory behaviours. It was developed over the course of the summer and saw a great deal of change over that time. We developed the database and UI, going through three major versions of the UI and two major versions of the database before settling on the final package – a Java client for students and a corresponding server and mySQL database. Study development occured late fall, with deployment near Christmas.
As of this writing, SCRL 1.0 is encountering a few hiccups as the students of our study approach their use of the tool, and we’re working to smooth them out so that further studies don’t have the same issues. We’ve got a database that’s growing quickly, and are looking forward to expansion to further groups. Planning is going well for this, and we hope to have a solid plan within a week.
SCRL 2.0 – Conceptual Design
We plan on making thorough examination of the data collected from SCRL 1.0 before deploying 2.0, but our design and development team still has a lot of work to do in order to fulfill our wish-list of features.
The major difference between SCRL 1.0 and 2.0 will be the inclusion of embedded analytics – we intend to examine student behaviours both within SCRL itself and while doing online reading and lecture-viewing. This will involve a completely new information pipeline that will capture and translate micro-scale system events, package them up as incomplete learning events, and then match them with competency assessment from sensor tools. Sensor tool data will also be polled at this time, bundling them along with the matching system events where possible.
Another feature we’re working towards will be embedded learning resources within SCRL. Educators will be able to embed learning resources in the tool for students, and students will be able to promote external content into the tools’ libraries for others to see. A sort of voting system will allow learners to grade the usefulness and enjoyability of these resources to create a hierarchy of topical courses. These resources will of course be under the auspices of the behaviour-capture methods spoken about above, providing another domain for SCRL to understand student self-reflection and engagement.
We have other hopes as well, but we consider these two to be sufficient for launch of SCRL 2.0.
SCRL 3.0 and Beyond
Once we have a useable platform that can capture student work and perform assessment of self-regulation, we should have a library of a number of sensor tools available – Java programming, English language writing, Finite state machines and some mathematics are a few examples we should have done by then. SCRL 3.0 and beyond will focus on creating a stable API for development of new sensors, as well as extending the Caliper and Tin Can/xperience APIs to contain regulation and motivation state information. We’ll also be working to bind SCRL with solid sentiment analytics, allowing deeper inspection of motivational states within learners.
Of course, we don’t want to plan too deeply into the future – we’ve already got enough on our plates, and we need to see the results from SCRL 1.0 and 2.0 before we can extend our grasp!
SCRL is right now a fancy data-collection and monitoring system – it’s sort of like a diary for students, to help them organize their learning and understand their motivations a little more explicitly. According to the Self-Regulation model, that on its own improves learning effectiveness. We plan on going much, much further than that, however. This is only the first few steps.