Building capability for online learning

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Introduction

Online learning demands a set of skills that may not be so important in a face-to-face environment. Technical computer skills are clearly important, but other behavioural skills may be just as important.

It has been reported from many sources (Diaz, 2002, Smith, Ferguson, 2005) that the rate of attrition in online courses is greater than that of traditional face-to-face courses. Pillay, Irving and Tones (2007) found that students are often less satisfied by online learning environments than classroom environments. Interestingly, a study done with students in the SUNY learning network found that the completion rates of their online courses were not significantly different from their face-to-face classes, and that their online students were at least as satisfied as their F2F students (Shea, Fredericksen, Pickett, Pelz, Swan, 2001). Student satisfaction seems to be correlated with course completion rates.

If we can determine the factors which predict satisfaction and achievement, and also attrition and non-achievement, we may be able to better accomodate the needs of online learners in our programme.

Pre-requisites for online learning success

According to Clark and Mayer (2004) online students need to have metacognitive skills. These are the ability to set learning goals, to determine how to reach their goals, and to make adjustments where necessary. Students with poor metacognitive skills need more direction where as students with good metacognitive skills tend to be more self-sufficient learners. This skill-set has been described elsewhere (Connor, 2004) as qualities of a "self-directed learner".

Pillay, Irving and Tones (2007) found that the following factors contribute to good outcomes for students

  • Social interaction
  • Computer Literacy
  • Computer self-effiacy (or the perception of the learner that they can be an effective computer user)
  • Positive online learner qualities

and that the following factors contribute to poor outcomes for online learners

  • A predetermined pace of learning
  • Poorly designed or poorly functioning learner experiences
  • Dissatisfaction (which may be related to a low level of computer self-effiacy, or a low level of interaction with the learning community and/or instructors)
  • Negative online learner qualities

Some of these qualities are related to educational design which is outside of the scope of this article, but others are related to student skills and attitudes that could potentially be developed through a study skills and computer literacy programme.


The Need for Study Skills & Computer Literacy Programmes

Pillay, Tones and Irving (2007) found that students who had completed computer literacy courses before engaging in online study were observed experiencing less anxiety and frustration than those who had not. They also found that computer self-effiacy is enhanced by the development of technical computer skills. A low level of computer self-effiacy is related to feelings of anxiety when required to use computer applications. This anxiety leads users to interpret events more negatively than non-anxious users and therefore contributes to dissatisfaction.

Well designed study-skills and computer literacy courses should increase the computer literacy and self-effiacy of students. In principle this should reduce frustration, anxiety and therefore dissatisfaction that is felt by students engaged in online courses. A lower rate of dissatisfaction should contribute to a lower attrition rate.

In the same research study (Pillay, et al, 2007) a number of learner qualities which are related to learner success and satisfaction online were identified. The ability to select appropriate study aids, effective time management and the ability to concentrate on the learning process despite any distraction that may occur are learner qualities that contribute to academic achievement in the online context. Distractions are legion in the flexible learning environment, and may range from the lure of the beach on a sunny day to the TV, children, partner, friends and family and many many more.

The learner qualities which are predictive of student dropout are the lack of ability to select the main ideas from educational experiences or articles, an attitude that the material studied was irrelevant to the student’s educational pathway and a lack of ability to resist distractions from the learning process. (Pillay, et al, 2007)

Making students aware of the ways in which they can be active in their learning process (e.g. through instruction in note-taking, setting goals, learning strategies, self-regulation, study groups) and designing student learning experiences so that they utilise student choice such as project-based learning or research exercises may help students to develope meta-cognitive and self-directed learning skills. (Connor, 2004).

Students enrolled in study skills courses that operate in isolation from the main course tend to develop skills that often do not transfer well to their study context. Study skills courses are more beneficial when integrated with course activities (Wingate, 2006).

Conclusion

In accordance with these findings, an online learning course should begin with an initial screening to determine areas of learner weakness and strength. Study skills/computer literacy courses should ideally be customised to the individual needs of each student. These courses should focus on the development of traditional study skills (e.g. developing the ability to select the main ideas from educational experiences or articles), computer literacy and the learner qualities which are important to online success. In addition to this the study skills/computer literacy courses should be contextualised within the student's primary programme of study.

Resources

If the intention is to individualise study skills and computer literacy programmes, a library of modular learning resources is needed.

Examples of screening forms/self-assessments used by online educational institutions

Examples of open study skills and/or computer literacy programmes for online learning

References

Clark, R., & Mayer, R. (2004). E-Learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. CA, USA: Pfeiffer.

Connor, C. (2004). Developing self-directed learners. Oregan, USA: Northwest Regional Educational Laboratory. Retrieved on November 11, 2007 from http://www.nwrel.org/planning/reports/self-direct/index.html

Diaz, D. (2002). Online dropout rates revisited. Retrieved on September 29, 2007 from http://www.technologysource.org/article/online_drop_rates_revisited/

Pillay, H., Irving, K., & Tones, M. (2007). Validation of the diagnostic tool for assessing Tertiary students’ readiness for online learning. Higher Education Research & Development, 26:2, 217 - 234

Shea, P., Fredericksen, E., Pickett, A., Pelz, W., & Swan, K. (2001). Measures of learning effectiveness in the SUNY learning network. In J. Bourne, & J. Moore (Eds.), Online Education - Volume 2 - Learning Effectiveness, Faculty Satisfaction and Cost Effectiveness - Proceedings of the 2000 Summer Workshop on Asynchronous Learning Networks. Massachusetts, USA: Sloan Centre for Online Education.

Smith, G., & Ferguson, D. (2005). Student attrition in mathematics e-learning. Australasian Journal of Educational Technology, 21:3, 323-334. Retrieved on September 29, 2007 from http://www.ascilite.org.au/ajet/ajet21/smith.html.

Wingate, U. (2006). Doing away with ‘study skills’. Teaching in Higher Education, 11, 457-469.

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