Current complicated learning conditions require learners to do complex cognitive tasks in comparison to conventional learning environments. Due to the limited processing capability and confined human working memory, cognitive overload occurs and new i...
Current complicated learning conditions require learners to do complex cognitive tasks in comparison to conventional learning environments. Due to the limited processing capability and confined human working memory, cognitive overload occurs and new information is extinguished before being stored in long-term memory. To reduce cognitive overload and allow effective learning, the characteristics of working memory have to be considered. In this respect, cognitive load theory can be one possible answer for solving complex cognitive tasks.
Cognitive load theory has been focused the context of multimedia learning in most studies. Recently, however, prior knowledge, learning motive, and problem solving ability of the learner have been considered factors affecting cognitive load reduction.
Prior knowledge has mentioned as a main factor for deriving flow as well as cognitive load. The flow refers to the optimal mental state occurring when the leaner devotes attention to a certain activity. The flow can be easily reached when difficulty degree and goal are suitable for the learner’s level. At this time, effective learning results can be obtained by considering Prior knowledge of the learner. Cognitive load theory is mentioned as the fundamental principle which does not interrupt the flow, and more effective learning can be expected when learning flow is reached by preventing cognitive overload (the Korea Education & Research Information Service, 2005). As such, cognitive load and flow are main factors that are affected by prior knowledge and are capable of determining the success/failure of learning. None of the studies on this issue, however, has yet considered all of prior knowledge, cognitive load, and flow. Therefore, this study reviewed previous literature on this issue, and determined whether prior knowledge, cognitive load, and flow could be meaningful influences upon learning achievement.
By reflecting such study objectives, detailed study questions to be answered in this study are as follows:
1. Is the relationship among cognitive load(cognitive effort, error at the time of problem solving), flow(attention, interest, curiosity) and achievement according to prior knowledge of the learners suitable for a model of structural equation?
2. Does prior knowledge predict cognitive load(cognitive effort, the time of problem solving) and flow(attention, interest, curiosity)?
3. Do prior knowledge, cognitive load(cognitive effort, error at the time of problem solving), and flow predict achievement?
4. Do cognitive load(cognitive effort, error at the time of problem solving) and flow(attention, interest, curiosity) mediate a relationship between prior knowledge and achievement?
The subjects of this study were 164 students of Korean modern & contemporary history class in Y girls’ high school located in Suwon-si, Kyounggi-do. The study lasted for five weeks from October to November in 2007, and prior knowledge, learning achievement, cognitive load, and flow were measured.
This study has reached the following conclusions:
First, as a result of correlation analysis, it has proved that correlation exists among all variables and, in particular, the time of problem solving and learning achievement show high correlation (r= .78). Prior knowledge and learning achievement also show high correlation (r= .78), and cognitive effort and achievement also show relatively high correlation (r= .67). Based on the result, it has proved that achievement, a learning result variable, correlates with cognitive load and flow, mediating variables, as well as independent variables. Considering other variables, prior knowledge and cognitive effort show middle correlation (r= .47) and prior knowledge and the time of problem solving also show middle correlation (r= .528). Prior knowledge shows middle correlation (r= .465) with flow, middle correlation (r= .379) with interest, and correlation (r= .463) with curiosity. Flow shows relatively high correlation (r= .612) with achievement. Flow shows middle correlation (r= .534) with interest and middle correlation (r= .593) with curiosity.
Second, since prior knowledge of the learner meaningfully predicts cognitive load (β = .56) and flow (β = .54), sub-variables of cognitive load and flow are also considered. It has proved that cognitive effort and the time of problem solving, which are sub-variables of cognitive load, and attention, interest, and curiosity, which are sub-variables of flow, are predicted by prior knowledge. By considering all the results, it can be seen that prior knowledge is a variable predicting cognitive load and flow, which has a thread of connection with the research on the positive influence of prior knowledge upon cognitive load and flow (Cook, 2006; Trevino, 1993).
Third, it has proved that prior knowledge, cognitive load, and flow predict achievement. As a result of analysis of variance for a model, it has proved that the estimated model is meaningful and prior knowledge, cognitive load, and flow describe 78.6% of achievement (F(3, 160)=195.49, p < .001). More specifically, prior knowledge (β = .38) is a variable predicting the most part of achievement, and cognitive effort (β = .11), the time of problem solving (β = .13), interest (β = .24), and curiosity (β = .26) are also variables predicting achievement, whereas attention fails to predict achievement even at a meaningful level of .05.
Fourth, to determine whether cognitive load and flow act as mediating variables between prior knowledge and achievement, hierarchical regression analysis was conducted. Since a relationship between cognitive load and flow has not been considered in this study, mediating effects obtained by applying cognitive load and flow respectively were examined. First, to determine whether cognitive load acts as a mediating variable, cognitive effort and the time of problem solving, which are sub-variables of cognitive load, are applied. As a result, it has proved that both of cognitive effort and the time of problem solving have mediating effects. Second, to determine whether flow acts as a mediating variable, attention, interest, and curiosity, which are sub-variables of flow, are applied. As a result, it has proved that all of attention, interest, and curiosity have mediating effects.
This study focuses on whether the learner's prior knowledge has an effect on cognitive load and flow rather than suggesting methods of stimulating the learner's prior knowledge, reducing cognitive load and inducing flow. Furthermore, the relationship between priorknowledge and achievement, cognitive load and achievement, and flow and achievement are investigated on the whole.
The suggestions of this study are as followings.
First, it is necessary to study the effects of subordinate variables of the learner's prior knowledge and achievement.
Second, there should be a profound study between flow and cognitive load. The study between flow and cognitive load is not well prepared and the concrete study is not conducted yet. Accordingly, after studying the relationship between flow and cognitive load, the explicit solution should be suggested to raise study achievement.
Third, apart from the learner's prior knowledge, flow and achievement, the main variables such as difficulty, reality and study motive that are related to cognitive load should be studied.