When Instructional Guidance is Needed

Discussion of the special issue on cognitive load theory

BACKGROUND

The present Special Issue contains 9 papers exploring novel cognitive load theory research questions.

AIM

To provide a discussion of the 9 articles in this Special Issue of the Journal and to highlight the strengths and weaknesses of the papers and indicate opportunities for future studies.

PROCEDURE

Briefly summarizes cognitive load theory in its current version followed by the discussion of the 9 individual papers.

Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room

OBJECTIVE

The aim of this work was to examine (electroencephalogram) EEG features that represent dynamic changes in the functional brain network of a surgical trainee and whether these features can be used to evaluate a robot assisted surgeon's (RAS) performance and distraction level in the operating room.

MATERIALS AND METHODS

Electroencephalogram (EEG) data were collected from three robotic surgeons in an operating room (OR) via a 128-channel EEG headset with a frequency of 500 samples/second. Signal processing and network neuroscience algorithms were applied to the data to extract EEG features. The SURG-TLX and NASA-TLX metrics were subjectively evaluated by a surgeon and mentor at the end of each task. The scores given to performance and distraction metrics were used in the analyses here. Statistical test data were utilized to select EEG features that have a significant relationship with surgeon performance and distraction while carrying out a RAS surgical task in the OR.

RESULTS

RAS surgeon performance and distraction had a relationship with the surgeon's functional brain network metrics as recorded throughout OR surgery. We also found a significant negative Pearson correlation between performance and the distraction level (-0.37, p-value < 0.0001).

CONCLUSIONS

The method proposed in this study has potential for evaluating RAS surgeon performance and the level of distraction. This has possible applications in improving patient safety, surgical mentorship, and training.

Element interactivity as a factor influencing the effectiveness of worked example-problem solving and problem solving-worked example sequences

BACKGROUND

The worked example effect in cognitive load theory suggests that providing worked examples first followed by solving similar problems would facilitate students' learning. Using problem solving-worked example sequence is another way of implementing example-based instruction. Although research has demonstrated the superiority of worked example-problem solving sequence on learning materials that presumably are high in element interactivity for novices, none of the previous studies have compared the two sequences with levels of element interactivity experimentally manipulated in a strictly controlled manner.

AIM

The reported study aimed to investigate the effects of levels of element interactivity of the learning tasks and levels of learner prior knowledge on the effectiveness of two alternative example-based sequences, worked example-problem solving versus problem solving-worked example.

SAMPLE

Fifty-two Year five students, around 10 to 11 years old, from a primary school in Indonesia participated in Experiment 1, and 96 Year eight students, around 13 to 14 years old, from a secondary school in Indonesia participated in Experiment 2.

METHODS

2 (sequences: worked example-problem solving vs. problem solving-worked example) × 2 (levels of element interactivity: low vs. high) experimental design, with the second factor repeatedly measured, was used in the two experiments conducted with learners at different levels of prior knowledge.

RESULT

The results showed the advantage of using worked example-problem solving sequence for learning materials high in element interactivity, especially for novice learners, whereas there were no differences between the worked example-problem solving and problem solving-worked example sequences for learning materials low in element interactivity for more knowledgeable learners.

CONCLUSION

This study not only replicated the results of previous studies, but also extended their findings by experimentally manipulating levels of element interactivity of learning materials.

Undesirable Difficulty Effects in the Learning of High-Element Interactivity Materials

According to the concept of desirable difficulties, introducing difficulties in learning may sacrifice short-term performance in order to benefit long-term retention of learning. We describe three types of desirable difficulty effects: testing, generation, and varied conditions of practice. The empirical literature indicates that desirable difficulty effects are not always obtained and we suggest that cognitive load theory may be used to explain many of these contradictory results. Many failures to obtain desirable difficulty effects may occur under conditions where working memory is already stressed due to the use of high element interactivity information. Under such conditions, the introduction of additional difficulties may be undesirable rather than desirable. Empirical evidence from diverse experiments is used to support this hypothesis.