Motivation and technological self-efficacy

2.3.4.1 Theories on motivation

One of the determinants of the level or degree of motivation is people’s belief in their capabilities to influence the way they think and behave – self-efficacy. Self-efficacy, as part of an entire self-belief system, “varies across activity domains and situational conditions rather than manifest [sic] uniformly across tasks and contexts in the likeness of a general trait” (Bandura, 2012). It allows people to achieve a level of performance by influencing their approach to dealing with events in their lives (Bandura, 1994). Bandura’s view is that, there is a clear link between self-efficacy and self-regulation of motivation and behaviour, goal systems, outcome expectations, perceived environmental facilitators and enablers, and environmental impediments. Self-efficacy, therefore, is embedded and plays a part in social

2012). According to Bandura, positive and negative orientations to self-efficacy are affected by cognitive, motivational, and affective influences. People with a strong sense of perceived self-efficacy generally have a positive outlook and therefore, task performance is enhanced because challenges are seen as something to be mastered rather than as threats to be avoided. The converse is true of people with low self- efficacious beliefs of their capabilities and therefore, they have a weak commitment to goals and will tend to dwell on their personal deficiencies.

Cognitive and/or learning styles define the way learners will organise information, and in general, a person with strong self-efficacy will undertake purposive and self-appraisal strategies to facilitate their learning, control their emotional and motivational reactions towards goal-oriented outcomes, employ strategies to attain mastery of skills, and create environments for themselves that will help develop their competencies (Bandura, 1994). Mistler-Jackson and Songer (2000) probed students’ attitudes and motivation in relation to the use of internet-rich curricular science programs to determine whether students with a certain motivation profile showed greater content learning than others. The study showed that high, moderate and low motivation learners demonstrated corresponding mean scores in terms of their content knowledge; in particular, the lowest achieving group showed the least coherence and accuracy of their knowledge. Mistler-Jackson and Songer (2000) also discovered that the affordances provided by internet technologies increased high school students’ self-efficacy and motivation in Science learning and that achievement in goal-oriented outcomes is largely influenced by a belief in their capabilities to influence the way they think and behave. Motivation to this end is seen as a product of expectancy (to achieve a goal) and value (need or importance of the goal).

2.3.4.2 Motivation and technology

The relationship and degree of interplay between people’s belief in their capability to achieve a goal (self-efficacy), their existing or current capability to address and complete a learning task (eductive ability) and the value that they place on the task or goal is less than clear when the limitations and affordances of

technologies used for learning are considered. Astleitner (Astleitner & Leutner, 2000; Astleitner, Brunken, & Leutner, 2003; Astleitner & Wiesner, 2004; Astleitner, 2005) proposed principles for designing technology-based learning and instruction that considers the motivational elements of fear, anger, envy, sympathy and pleasure to achieve effective learning. Argumentation, which is a higher order skill, was proposed as an effective interactive online strategy especially when motivational support is provided. Therefore, affection and self-efficacy can be essential, not only for adoption of technology, but to support critical thinking.

Roger’s (1962) Diffusion of Innovation theory states that a person’s ability to adopt a new innovation will depend on their willingness (motivation) or ability to adopt a new innovation. People are influenced by knowledge or awareness, interest or attitude, evaluation of an experience, trial or use of an innovation, and their previous adoption experiences. Based on a bell curve distribution, Rogers

categorised adopters as innovators (2.5%), early adopters (13.5%), early majority (34%), late majority (34%) and laggards (16%). The characteristics of each category of adopter include: innovators - venturesome, educated, multiple information

sources; early adopters - social leaders, popular, educated; early majority -

deliberate, many informal social contacts; late majority - sceptical, traditional, lower socio-economic status; and laggards - neighbours and friends are the main sources of information, fear of debt (Rogers, 1962).

Adoption of innovation has two influencing factors, the need for cognition and the need for change (Wood & Swait, 2002). In a study in which Wood and Swait (2002) investigated consumer characteristics, the need for cognition and need for change, and the consumer’s capacity to change and handle innovation was found to affect an individual’s propensity to adopt new innovations versus the status quo. Their findings are consistent with a number of humanistic and socio-cultural theories such as Maslow’s Hierarchy of needs (Maslow, 1968) which classified lower level or deficiency (physical, security, friendship and love, and esteem) and higher level or self-actualisation needs, with the latter consisting of intellectual achievement, aesthetic appreciation, creativity and problem-solving.

2.3.4.3 Motivation and learning

Another aspect of motivation which was alluded to in Section 2.3.3, relates specifically to the strategies to learning that one employs when motivation exists. Ames and Archer (1988) found that, “When students perceived their class as

emphasizing a mastery goal, they were more likely to report using effective learning strategies, prefer tasks that offer challenge, like their class more, and believe that effort and success covary”. Biggs (1987) extended Entwistle’s (1979) information processing style inventory to add an achievement dimension to motivation and posits that both surface and deep learning profiles can combine with an achieving approach so that a student may aim to achieve top marks by using surface strategies such as memorising. Another study of 1,266 Australian students showed that learning strategies are congruent with their motivation for learning and that such congruence is associated with higher average school grades (Watkins & Hattie, 1992).

Thus when needs are met, a person’s motivation increases towards further improvement or goals. In addition to a learner’s innate abilities (eductive ability), goal setting can improve performance (critical thinking) because it allows one to focus on the task, encourages greater effort to perform, increases persistence, and promotes the development of new strategies (approach to learning and motivation) especially when previous ones have failed (Locke & Latham, 1990).