Data analysis Essay

Custom Student Mr. Teacher ENG 1001-04 19 May 2017

Data analysis

I also thought that the target groups characteristics might interact with instructional formats as well as with the learning materials complexity. I drew this assumption from the Feedback Intervention Theory (FIT). In their meta-analysis on learning from feedback, Kluger and DeNisi (1996) tested several moderators, moderating the relationship between interpreting feedback as occurring in the experiential learning circle, and performance improvement.

These moderating variables are related to person related variables such as cognitive ability, self-efficacy, or locus of control, the form of feedback intervention (feedback intervention, such as sign, content and so on), task characteristics (novelty, complexity, and task duration, etc. ), and situational variables (goal setting, etc. ). The target learner group is thought to be an important person-related factor, because learning with simulation already requires some of the skills which are also defined as learning process related outcomes like, for instance, reflective observation and abstract conceptualization (Kolb, 1984).

In addition, for example, numerical abilities and analytical thinking to infer relations between variables are important faculties which the learner should possess, as well as a positive attitude toward his/her learning potential. Intelligence facets like reasoning or working memory should also be considered important predictors for successful learning with simulations (see Wittmann & Hattrup, 2004; and Wittmann & Su? , 1999).

Besides cognitive abilities as prerequisites, error management strategies (see Hesketh & Frese, 2002; Ivancic & Hesketh, 1996, 2000; Keith & Frese, 2005) are needed, which allow learners to cope with, and recover from, error situations that may have negative motivational consequences. One can expect learners already equipped with these competencies to have an advantage over others. In that respect, it may be assumed that preconditions for effectiveness in experiential learning are to some extent the skills and abilities to which most attention should be paid throughout the learning process.

Besides reasoning and working memory capacity, performance of complex activities such as simulation-based learning is strongly influenced by personal efficacy (Bandura, Debowski, & Wood, 2001). Following Bandura (1997; Bandura & Wood, 1989; Wood & Bandura, 1989), self-efficacy functions as a moderator between learning and performing. Self-efficacy is a judgment of one’s ability to organize and execute given types of performance.

Efficacy beliefs have an affect upon patterns that can enhance or undermine performance. Perceived efficacy exerts a more substantial impact on performance, both directly by affecting the quality of thinking and the good use of cognitive skills and indirectly by strengthening persistence in the search for solutions. Perceived efficacy affects problem-solving, performance, both directly and by its impact on other self-regulatory determinants (Bandura, 1997).

Such beliefs influence the choices people make, their goal aspirations, how much effort they mobilize in a given endeavor, how long they persevere in the face of difficulties, their vulnerability to stress and depression in coping with taxing demands, and their resilience to adversity (Debowski, Wood, & Bandura, 2001). The complexity level of the simulation is based on the FIT thought to be a second important influential variable for the learning outcome, bearing in mind that the learning process requires inferring the characteristics of the model underlying the simulation.

The process of inference implies the observation of the output variable changes in value after altering values for the input variables. The higher the interconnectivity between input variables and output variables, or the interconnectivity also between output variables, the more difficult the inferring process becomes (see Wood, 1986). Variable characteristics like parallel effects are especially difficult to perceive, because they are often mixed with the direct effects of another input variable (Funke, 1993, 2001; Kluge, in press, 2004).

Other characteristics like dynamic changes are easier to identify but more difficult to control. All changes in the system conditions, such as exponential growth or decline, that occur of their own accord, without intervention by the problem solver are called dynamic changes. Finally, a third variable of interest which will influence the learning outcome is assumed to be the kind of support method used. It is expected that learning variations of experiential learning in terms of learning support affects the target groups differently.

To test this hypothesis for experiential learning, in this study two methods of providing additional support: 1) support for the design of experiments, and 2) direct access to domain knowledge) are tested against each other in two different simulations. Both support methods make use of the model progression technique (see above). The support for the design of an experiment is operationalized by guided exploratory search. It is assumed that individuals who are on the threshold of learning are often unable to reach an understanding without some kind of externally provided assistance or intervention.

The support methods presented are assumed to vary in respect to the simulation complexity as well. The higher the interconnectivity between variables, the more effective will be a support method that allows direct access to domain knowledge. In summary, the potential for using learning in simulation-based environments is assumed to vary according to at least three factors: the target groups, the complexity of the simulation, and the instructional support given.

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