The Expertise Reversal Effect is a Variant of the More General Element Interactivity Effect

Sweller

Intern. J. Comput.-Support. Collab. Learn

et al. 2018

Self Cite

326

263

Cognitive load theory has traditionally been associated with individual learning. Based on evolutionary educational psychology and our knowledge of human cognition, particularly the relations between working memory and long-term memory, the theory has been used to generate a variety of instructional effects. Though these instructional effects also influence the efficiency and effectiveness of collaborative learning, be it computer supported or face-to-face, they are often not considered either when designing collaborative learning situations/environments or researching collaborative learning. One reason for this omission is that cognitive load theory has only sporadically concerned itself with certain particulars of collaborative learning such as the concept of a collective working memory when collaborating along with issues associated with transactive activities and their concomitant costs which are inherent to collaboration. We illustrate how and why cognitive load theory, by adding these concepts, can throw light on collaborative learning and generate principles specific to the design and study of collaborative learning. Intern. J. Comput.-Support. Collab. Learn (2018) 13:213-233 https://doi

Section: Instructional Designmentioning

confidence: 99%

From Cognitive Load Theory to Collaborative Cognitive Load Theory

Sweller

Intern. J. Comput.-Support. Collab. Learn

et al. 2018

Self Cite

326

263

The Educational and Developmental Psychologist

“…In the current experiment, we did not vary expertise, but rather varied element interactivity. Since increases in expertise result in decreases in element interactivity, our results may be due to exactly the same factors that lead to the expertise reversal effect (Chen, Kalyuga, & Sweller, 2016b). …”

mentioning

confidence: 71%

When Instructional Guidance is Needed

Chen

Kalyuga

Sweller

2016

Self Cite

Studying worked examples providing problem solutions to learners usually leads to better test performance than solving the equivalent problems without guidance, demonstrating the worked-example effect. The generation effect occurs when learners who generate answers without guidance learn better than those who read answers that provide guidance. The contradiction between these results can be hypothesised to be due to differences in the element interactivity of the learning tasks. Primary school students in Year 6 participated in the experiment, which investigated the hypothesis by using geometry materials. A disordinal interaction was obtained between levels of guidance and levels of element interactivity. Higher levels of guidance facilitated learning using high element interactivity information, while lower levels of guidance facilitated learning for low element interactivity information. Cognitive load theory was used to explain these contrasting results. From an educational perspective, it was suggested that when determining levels of guidance, a consideration of element interactivity is essential.

New Pedagogical Challenges in the 21st Century - Contributions of Research in Education

“…An element refers to anything that requires learning (e.g., a number, a symbol, a concept, a procedure, etc.) [44]. Under this conceptualization of cognitive load theory, the level of element interactivity determines the extent to which a particular type of cognitive load would exert its influence on the design of an instruction.…”

Section: Cognitive Load Theory: a Theoretical Overviewmentioning

confidence: 99%

Achievement Bests Framework, Cognitive Load Theory, and Equation Solving

Ngu¹,

Phan²

2018

The Framework of Achievement Bests provides an explanatory account into the process of optimization, which details how a person reaches from one level of best practice to that of a more optimal level. This framework, we contend, is significant in its explanatory account of personal growth, an internal state of flourishing, and the achievement of exceptionality. This chapter conceptualizes the applicability of the Framework of Achievement Bests to the context of instructional designs. We highlight the tenet of element interactivity, which is integral to the design of a particular mathematics instruction and its potential effectiveness. Element interactivity entails the interaction between elements within a learning material. Owing to the limited working memory capacity, an instruction that incurs high level of element interactivity would impose high cognitive load leading to reduced learning. Our conceptualization postulates the possible alignment between suboptimal and optimal instructional designs with realistic and optimal levels of best practice, respectively. This postulation (e.g., suboptimal instructional design → realistic level of best practice), which recognizes the importance of cognitive load imposition, is significant from a practical point of view. By focusing on instructional designs, it is possible to assist individuals to achieve optimal best practice in learning.