Self-Regulated Leaning Skills for Students Online: What are they and how are they measured?

Online learning asks more of students than traditional classroom settings. The classroom brings students and instructors together in a common room and time enabling direct communication and direction from the instructor. The students can be passive in that once they are in class, the information is delivered. Online students must be proactive in bringing themselves to the lessons and recognizing that they are entirely responsible for their success.

Hung et al. (2010) described five personal skills of successful online students. Self-directed learning is a measure of a student’s ability to establish personal goals, identify the necessary resources, schedule their work, and in general, take personal responsibility. Motivation for learning includes intrinsic characteristics like personal interest or curiosity, and extrinsic motivations to receive rewards. Learner control refers to the inherent flexibility of asynchronous online learning where students can choose specific activities, the time spent on activities, or the activity sequence. Computer and Internet self‑efficacy measures the ability to use the Internet to accomplish tasks rather than just technical ability. Online communication self‑efficacy is the ability to effectively use the technology to participate in intellectual discourse with classmates and instructors.

Yang and Park (2011) described a study in which the instructional design embedded self‑directed learning skills and communication self‑efficacy skills into an online class. They reported that based on post course surveys, students’ ability in self‑directed learning skills improved but not so for communication self‑efficacy skills. They postulate that these students may not value peer interactions as an important aspect of individual performance.

Higher education institutions try to alert students to the demands of online learning through documents or websites describing the skills of online learners, and by surveys that ask students directly or indirectly about these skills. Both of these methods are common but have limited success. Poorly designed surveys may not ask the right questions of fully impress upon students the importance for self‑regulated learning skills (Hall, 2008). Indiana University offers students white paper describing personal, academic, and technical skill needed to succeed online but again (Alford & Lawson, 2009) but these need to be read and fully understood by students to make a difference.

Given that many students will enter online classes unprepared, researchers attempt to understand student learning in courses through surveys and interviews. Usually these surveys are given before or after the class, and therefore miss the details of student progress through specific lessons (Winne, 2012). A properly designed computer based learning environment can trace student activity and infer the decision process through the lesson. In this way, instructors can assess both what the student learned, and how the student learned it. This can lead to better instructional design tools that help students adopt the skills of self‑regulated learners.


Works Cited

Alford, P, & Lawson, A. (2009). Distance Education Student Primer: Skills for Being a Successful Online Learner. Indiana University. Retrieved June 30, 2012 from

Michael Hall. (2008). Predicting Student Performance in Web-Based Distance Education Courses Based on Survey Instruments Measuring Personality Traits and Technical Skills. Online Journal of Distance Learning Administration, 11(3). Retrieved from

Hung, M.-L., Chou, C., Chen, C.-H., & Own, Z.-Y. (2010). Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 55(3), 1080–1090. doi:10.1016/j.compedu.2010.05.004

Winne, P. H. (2010). Improving Measurements of Self-Regulated Learning. Educational Psychologist, 45(4), 267–276. doi:Article

Yang, Y.-C., & Park, E. (2011). Applying Strategies of Self-Regulation and Self-Efficacy to the Design and Evaluation of Online Learning Programs. Journal of Educational Technology Systems, 40(3), 323–335. doi:Article


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