Although these features have allowed TSE to remain relevant through the times, this definitional breadth has also created confusion and a proliferation of related constructs.
This is commonly referred to as distance education and implementation ranges from courses being supported by the web (teaching occurs predominantly through face-to-face instructor interactions with supplemental materials being offered on the web) to blended learning (significantly less face-to-face instructor interactions and more online instruction) to fully online (all instruction is conducted virtually with no face-to-face instructor interactions).
A number of advantages are associated with distance learning such as increased flexibility and convenience, which allows individuals the opportunity to enroll in classes that would otherwise be off-limits due to geographical or personal reasons.
[10] Another commonly cited advantage is that instruction is self paced, which allows for personalized tailoring based on individual needs.
[11] However, these advantages are not likely to be realized if the individual is anxious about the method of instructional delivery and/or his or her expectation of success is low due to its technological component.
This makes sense given both types of specific self-efficacies are related to using tools albeit one being technology the other being more physical in nature.
Specifically, measures of self-efficacy must be self-report because the only person who can accurately portray beliefs in one's ability is the target of investigation.
While a number of problems exist with self-report inventories, in the case of self-efficacy (and other constructs that are defined as internal beliefs and cognitions) this measurement approach is unavoidable.
While the type of measurement approach is defined by the construct, the process of developing and validating these scales has varied considerably throughout the TSE literature.
This scoring approach asks participants to rate how confident they are in completing the task(s) on a numerical scale and then averages across all items.
This consideration is similar to the previous differentiation between-TSE as a broader concept and technology specific self-efficacy.
McDonald and Siegall[1] developed a five-item likert scale of technological self-efficacy based on the consideration of previous theoretical studies.
[3] These authors reviewed previous attempts to measure computer self-efficacy and theoretically derived a 10-item scale.
[15] For each item, participants were first asked whether they could complete a specific task related to computers using a dichotomous yes/no scale.
Similar to previous measurement approaches, internet self-efficacy was developed using a theoretical approach that considered previous measures of related topics and developed novel items to address the missing construct space.
Bandura[2] proposes four primary sources for self efficacy beliefs; (1) prior experience, (2) modeling, (3) social persuasions, and (4) physiological factors.
[21][22][23][24] Although different types of training interventions have been associated with different gains;[25] in general, research supports that seeing other individuals successfully perform the task at hand (for example, the instructor) and then providing the learner with some opportunity for reinforcement and demonstration (for example, trying to successfully utilize the technology without aid) increases technology related self efficacy beliefs.
Social persuasions such as encouragement by others[17] and organizational support[17][26][27] are also important contributors to technology related self efficacy beliefs.
[36] However, older adults' low technological self efficacy beliefs suggest that older adults may internalize the 'old dogs can't learn new tricks' stereotype, which consequently affects expectations about future performance in technology related domains.
Other scholars have behavioral intentions to act as a mediator between TSE and other outcome variables (performance).