Levels Of Cognitive Load Research Articles

The effect of cognitive load on Detection-Response Task (DRT) performance during day- and night-time driving: A driving simulator study with young and older drivers

Over the past 25 years, distracted driving and driver inattention have been one of the main risk factors for road traffic safety. Previous research has shown that increased cognitive load from non-visual activities take our mind off the driving task, by engaging our working memory resources. Studies have shown that engagement in these tasks results in a more concentrated gaze towards the road centre, reducing lateral deviation (e.g., reduced standard deviation of lane position (SDLP) and less high frequency steering reversals). The Detection-Response Task (DRT) is an International Organization for Standardization (ISO) standard used to study the impact of cognitive load, due to secondary task, on performance. This driving simulator study examined the effect of varying levels of cognitive load (using the n-back task) on driving and DRT performance. The effect of age and lighting conditions was examined by comparing the performance of a younger group of drivers (Age Mdn = 22) with that of an older cohort (Age Mdn = 66) during day- and night-time driving conditions. Results showed that young drivers’ DRT performance was better at night-time compared to day-time, when compared to that of older drivers. Young drivers also performed better at the n-back task. Overall, an increase in cognitive load increased response time to the DRT task. In line with previous studies, an increase in cognitive load also reduced SDLP, but only in the absence of the DRT. These results provide new knowledge on drivers’ detection of peripheral stimuli during different lighting conditions, and how these are affected by a cognitively loading, non-visual, working memory task. The implications of these findings on real-world road safety are discussed.

Development of an AI-driven system for neurosurgery with a usability study: a step towards minimal invasive robotics

Abstract Recent advances in artificial intelligence have enabled promising applications in neurosurgery that can enhance patient outcomes and minimize risks. This paper presents a novel system that utilizes AI to aid neurosurgeons in precisely identifying and localizing brain tumors. The system was trained on a dataset of brain MRI scans and utilized deep learning algorithms for segmentation and classification. Evaluation of the system on a separate set of brain MRI scans demonstrated an average Dice similarity coefficient of 0.87. The system was also evaluated through a user experience test involving the Department of Neurosurgery at the University Hospital Ulm, with results showing significant improvements in accuracy, efficiency, and reduced cognitive load and stress levels. Additionally, the system has demonstrated adaptability to various surgical scenarios and provides personalized guidance to users. These findings indicate the potential for AI to enhance the quality of neurosurgical interventions and improve patient outcomes. Future work will explore integrating this system with robotic surgical tools for minimally invasive surgeries.

Ultrasound versus videos: A comparative study on the effectiveness of musculoskeletal anatomy education and student cognition.

Ultrasound imaging is a dynamic imaging technique that uses high-frequency sound waves to capture live images of the structures beneath the skin. In addition to its growing use in diagnosis and interventions, ultrasound imaging has the potential to reinforce concepts in the undergraduate medical curriculum. However, research assessing the impact of ultrasound on anatomy learning and student cognition is scarce. The purpose of this study was to compare the impact of ultrasound-based instruction versus narrated videos on students' understanding of anatomical relationships, as well as the role of intrinsic motivation, self-efficacy beliefs, and students' attitudes in this process. A booster course on anterior leg and wrist anatomy was offered to second-year medical students. A randomized controlled trial with a cross-over design allocated students to either an ultrasound-based teaching condition (cohort A) or a narrated anatomy video condition (cohort B). Next, participants were crossed to the alternative intervention. At the start of the study (T0), baseline anatomy knowledge, intrinsic motivation, self-efficacy beliefs, and spatial ability were measured. After the first intervention (T1) and at the end of the study (T2), both cohorts were administered an anatomy test, an intrinsic motivation scale, and a self-efficacy scale. In addition, each student was asked to fill out a perception survey after the ultrasound intervention. Finally, building on the cross-over design, the most optimal sequence of interventions was examined. A total of 181 students participated (cohort A: n = 82, cohort B: n = 99). Both cohorts performed comparably on the baseline anatomy knowledge test, spatial ability test, intrinsic motivation, and self-efficacy scale. At T1, cohort B outperformed cohort A on the anatomy test (p = 0.019), although only a small effect size could be detected (Cohen's d = 0.34). Intrinsic motivation and self-efficacy of both cohorts were similar at T1. At T2, the anatomy test, intrinsic motivation, and self-efficacy scale did not reflect an effect after studying either sequence of the interventions. Students reported favorably about the ultrasound experience, but also mentioned a steep learning curve. Medical students found the hands-on ultrasound sessions to be valuable, increasing their interest in musculoskeletal anatomy and ultrasound imaging. However, the addition of ultrasound did not result in superior spatial anatomy understanding compared to watching anatomy videos. In addition, ultrasound teaching did not have a major effect on student cognition. Ultrasound-based teaching of musculoskeletal anatomy is regarded as difficult to learn, and therefore it is hypothesized that too high levels of cognitive load might explain the presented results.

Cognitive Load Assessment of Air Traffic Controller Based on SCNN-TransE Network Using Speech Data

Due to increased air traffic flow, air traffic controllers (ATCs) operate in a state of high load or even overload for long periods of time, which can seriously affect the reliability and efficiency of controllers’ commands. Thus, the early identification of ATCs who are overworked is crucial to the maintenance of flight safety while increasing overall flight efficiency. This study uses a comprehensive comparison of existing cognitive load assessment methods combined with the characteristics of the ATC as a basis from which a method for the utilization of speech parameters to assess cognitive load is proposed. This method is ultimately selected due to the minimal interference of the collection equipment and the abundance of speech signals. The speech signal is pre-processed to generate a Mel spectrogram, which contains temporal information in addition to energy, tone, and other spatial information. Therefore, a speech cognitive load evaluation model based on a stacked convolutional neural network (CNN) and the Transformer encoder (SCNN-TransE) is proposed. The use of a CNN and the Transformer encoder allows us to extract spatial features and temporal features, respectively, from contextual information from speech data and facilitates the fusion of spatial features and temporal features into spatio-temporal features, which improves our method’s ability to capture the depth features of speech. We conduct experiments on air traffic control communication data, which show that the detection accuracy and F1 score of SCNN-TransE are better than the results from the support-vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), adaptive boosting (AdaBoost), and stacked CNN parallel long short-term memory with attention (SCNN-LSTM-Attention) models, reaching values of 97.48% and 97.07%, respectively. Thus, our proposed model can realize the effective evaluation of cognitive load levels.

The Effects of VR and TP Visual Cues on Motor Imagery Subjects and Performance

This study objectively evaluated the effects of Virtual Reality Visual Cues (VRVCs) and Traditional Plane Visual Cues (TPVCs) on motor imagery (MI) subjects and Brain-Computer Interface (BCI) performance when building a classification model for MI-BCIs. Four metrics, namely, imagery stability, brain activation and connectivity, classification accuracy, and fatigue level, were used to evaluate the effects of TPVCs and VRVCs on subjects and MI-BCI performance. Nine male subjects performed four types of MI (left/right-hand grip strength) under VRVCs and TPVCs while EEG and fNIRS signals were acquired. FBCSP and HFD were used to extract features, and KNN was used to evaluate MI-BCI accuracy. Rt-DTW was used to evaluate MI stability. PSD topography and the brain functional network were used to assess brain activation and connectivity. Cognitive load and fNIRS mean features were used to evaluate fatigue. The mean classification accuracies of the four types of MI under TPVCs and VRVCs were 50.83% and 51.32%, respectively. However, MI was more stable under TPVCs. VRVCs enhanced the connectivity of the brain functional network during MI and increased the subjects’ fatigue level. This study’s head-mounted VRVCs increased the subjects’ cognitive load and fatigue level. By comparing the performance of an MI-BCI under VRVCs and TPVCs using multiple metrics, this study provides insights for the future integration of MI-BCIs with VR.

Measuring cognitively demanding activities in pediatric out-of-hospital cardiac arrest

BackgroundThis methodological intersection article demonstrates a method to measure cognitive load in clinical simulations. Researchers have hypothesized that high levels of cognitive load reduce performance and increase errors. This phenomenon has been studied primarily by experimental designs that measure responses to predetermined stimuli and self-reports that reduce the experience to a summative value. Our goal was to develop a method to identify clinical activities with high cognitive burden using physiologic measures.MethodsTeams of emergency medical responders were recruited from local fire departments to participate in a scenario with a shockable pediatric out-of-hospital cardiac arrest (POHCA) patient. The scenario was standardized with the patient being resuscitated after receiving high-quality CPR and 3 defibrillations. Each team had a person in charge (PIC) who wore a functional near-infrared spectroscopy (fNIRS) device that recorded changes in oxygenated and deoxygenated hemoglobin concentration in their prefrontal cortex (PFC), which was interpreted as cognitive activity.We developed a data processing pipeline to remove nonneural noise (e.g., motion artifacts, heart rate, respiration, and blood pressure) and detect statistically significant changes in cognitive activity. Two researchers independently watched videos and coded clinical tasks corresponding to detected events. Disagreements were resolved through consensus, and results were validated by clinicians.ResultsWe conducted 18 simulations with 122 participants. Participants arrived in teams of 4 to 7 members, including one PIC. We recorded the PIC’s fNIRS signals and identified 173 events associated with increased cognitive activity. [Defibrillation] (N = 34); [medication] dosing (N = 33); and [rhythm checks] (N = 28) coincided most frequently with detected elevations in cognitive activity. [Defibrillations] had affinity with the right PFC, while [medication] dosing and [rhythm checks] had affinity with the left PFC.ConclusionsFNIRS is a promising tool for physiologically measuring cognitive load. We describe a novel approach to scan the signal for statistically significant events with no a priori assumptions of when they occur. The events corresponded to key resuscitation tasks and appeared to be specific to the type of task based on activated regions in the PFC. Identifying and understanding the clinical tasks that require high cognitive load can suggest targets for interventions to decrease cognitive load and errors in care.

"Choose it, and remember it": The impact of choice on destination memory.

Destination memory can be defined as the capacity to remember to whom we transmit information. It is measured through the accuracy of retrieving the association between the information we transmit and the person to whom we transmit it. A destination memory procedure aims to emulate human interaction by sharing facts with celebrities (i.e., familiar faces) since we often communicate with people we know. However, the role of the choice about who we intend to transmit the information to has not been evaluated before. This paper investigated whether deciding with whom to share a piece of information benefits destination memory. We designed two experiments with different levels of cognitive load, increasing it from Experiment 1 to Experiment 2. The experiments included two conditions: the choice condition, in which participants chose from two options to whom they desired to share a fact, and the no-choice condition, in which participants simply shared facts with celebrities without the possibility of a choice. Experiment 1 suggested that a choice component did not affect destination memory. However, when in Experiment 2 we raised the cognitive load by increasing the number of stimuli, we found that selecting the recipient during the more challenging task provided an advantage in destination memory. This result is congruent with the explanation that the shift of the participants' attentional resources to the recipient, caused by the choice component, leads to a destination memory improvement. In sum, it seems that a choice component can improve destination memory only under demanding attentional conditions.

Design of head-up display interfaces for automated vehicles

This study aimed to identify which information should be displayed on a head-up display (HUD) in semi-automated vehicles to enable the driver to maintain better situational awareness during the manual operation of the vehicle. It further explored how does the size of the field of view of the HUD affects such information presentation, while taking into consideration user's personal preferences and opinions. Four head-up display interfaces were developed differing in the amount, frequency and field of view of visual information presentation, and were implemented in a simulated semi-automated vehicle. The HUDs were evaluated in a user-study with a within-subject design with 30 participants. The obtained results revealed that versions with smaller and larger field of view HUDsevoke similar driving performance, levels of cognitive load, user experience and perceived usability, suggesting that the HUD's field of view size does not have an overall significant effect on the driver's situational awareness. The results reveal that display of information, which can help with obtaining and maintaining higher situational awareness levels, contribute to better driving performance. Drivers on the other hand, prefer also display of information for lower situational awareness levels.

Investigating Students' Satisfaction with Online Collaborative Learning During the COVID-19 Period: An Expectation-Confirmation Model.

The recent outbreak of COVID-19 posed discontinuous disruption to traditional learning modes worldwide. In order to keep social distance, online collaborative learning has become a necessity during the pandemic. However, our understanding of students' well-being and satisfaction with online collaborative learning is limited, especially during the COVID-19 period. Leveraging expectation confirmation theory, this study focuses on the triggers and inhibitors of students' cognitive load during online collaborative learning process and their subsequent satisfaction with the learning mode during the pandemic. We used a mixed-method approach in this study. We conducted a qualitative study with interview data and a quantitative study with surveys. The results indicate several psychological and cognitive antecedents of students' cognitive load during online collaborative learning. Findings also indicate that a high level of cognitive load will decrease students' perceived usefulness of the online learning platform and expectation confirmation, thus leading to a low level of satisfaction with online collaborative learning. This study can provide theoretical and practical implications for a better understanding of online student groups' satisfaction with online collaborative learning during the COVID-19 period.

Cognitive workload affects ocular accommodation and pupillary response.

Cognitive involvement in reading causes variations in the tonus of autonomic nerve system. The aim of this study was to examine the effect of short-term cognitive load on accommodation and pupils' absolute values and temporal variability in test persons performing three different types of tasks. We aimed to show how cognitive tasks of different type and difficulty level affect accommodation and pupil behavior during a short time interval. Participants (n=58; mean age 16.4 years, SD=0.56) performed reading from a 10-inch LCD screen placed at 40cm distance. Three different types of tasks (numerical, textual, and the Stroop task), each at three different levels of cognitive load were introduced. Participants had 90s to complete each task. Accommodative and pupillary responses were measured with videoretinoscope Power Refractor 3 at 50Hz. Pupil size was largest in the Stroop task (M=5.20mm, SD=0.75mm), followed by the numerical tasks (M=5.02mm, SD=0.72mm) and textual tasks (M=4.78mm, SD=0.71mm). Accommodative fluctuations - measured as accommodation SD - were largest in the textual tasks (M=0.67 D, SD=0.34 D), followed by the numerical tasks (M=0.61 D, SD=0.40 D) and the Stroop task (M=0.52 D, SD=0.21 D). In our experiment, short-term cognitive load was associated with altered pupillary and accommodative response to near tasks. In conflicting tasks (Stroop) or in performing continuing calculations, the pupils were larger; in tasks requiring logical reasoning, the accommodative fluctuations were greater. These effects can potentially be associated with current near-point stress and myopia growth models.

Fusion of Spatial, Temporal, and Spectral EEG Signatures Improves Multilevel Cognitive Load Prediction

Cognitive load prediction is one of the most important issues in the nascent field of neuroergonomics, and it has significant value in real-world applications. Most of the previous studies of cognitive load prediction only utilized electroencephalography (EEG)-based spectral signatures or interchannel connectivity, ignoring abundant temporal microstate features, which may represent the transient topologies of EEG signals. Furthermore, previous studies have mostly focused on the binary-level classification of cognitive load for single-type cognitive tasks. To date, there are few studies on the multilevel prediction of cognitive load during mixed cognitive tasks. Here, we first designed a new paradigm termed the “finding fault game,” mixing multiple tasks of memory, counting, and visual search, and then developed a multidimensional analysis framework to improve cognitive load prediction using a fusion of spatial, temporal, and spectral EEG features. Specifically, EEG-based functional connectivity, microstates and power spectral densities (PSD) were calculated for three cognitive load levels. Twelve adult subjects participated in the study. The experimental results show that increased cognitive load was associated with elevated theta and degraded alpha power and significant changes in interchannel connectivity and microstates, and that fusing the three types of EEG features improved the performance of three-level cognitive load prediction, achieving the accuracies of greater than 80% in the cross-validation, real-time, and over-time prediction. The findings suggest that all three types of EEG features can serve as signatures of cognitive load and that their fusion can improve multilevel prediction.

Finding the missing: using statement analysis to assist in missing persons cases

PurposeThe purpose of this paper is to address the complexity of missing persons cases and highlight the linguistic differences that arise in this type of crime. Missing persons cases are typically very complex investigations. Without a body, crime scene forensics is not possible, and police are often left only with witness and suspect statements. Forensic linguistics methods may help investigators to prioritise or remove suspects. There are many competing approaches in forensic linguistic analysis; however, there is limited empirical research available on emerging methods.Design/methodology/approachThis research investigates Statement Analysis, a recent development in linguistic analysis that has practical applications in criminal investigations. Real-world statements of individuals convicted of or found to be not guilty of their involvement in missing persons cases were used in the analyses. In addition, Behaviour Sequence Analysis was used to map the progressions of language in the suspects' statements.FindingsResults indicated differences between the guilty and innocent individuals based on their language choices, for example, guilty suspects in missing [alive] cases were found more likely to use passive language and vague words because of high levels of cognitive load associated with the several types of guilty knowledge suspects in missing persons cases possess. Of particular interest is the use of untruthful words in the innocent suspects’ statements in missing [murdered] cases. While typically seen in deceptive statements, untruthful words in innocent statements may result because of false acquittals.Originality/valueThis research provides some support for Statement Analysis as a suitable approach to analysing linguistic statements in missing persons cases.

Graphic Organizers in an Online Learning Environment: Its Influence to Students’ Cognitive Load and Knowledge Gain

The study investigates how graphic organizers influence the level of cognitive load and knowledge gained by the students at Quezon City University during the 1st semester, Academic Year 2021–2022 in an online learning environment. The one-group pretest-posttest design was utilized in the study. Results show that by using graphic organizers, students’ level of cognitive load and knowledge gain have improved.

The success of the educational task for visual-motor coordination among virtual reality: cognitive-personal factors

Introduction. The study of individual factors affecting the effectiveness of learning in the middle of virtual reality is especially relevant in connection with the growing volume of the introduction of virtual simulators in the field of vocational education. The purpose of this study is to study the role of the cognitive-personal characteristics of students in the success of the educational task for visual and manous coordination among virtual reality. Materials and methods. The educational task in the environment of virtual reality was modeled according to the scenario of the game, in which the subject can shoot from virtual onions on moving targets, gaining points and levels depending on the accuracy and complexity of the actions performed. The success of the execution was evaluated by two indicators: the accuracy of the hits and the maximum level of complexity of the situations of the task. With the help of survey methods, 40 students of pedagogical undergraduate were evaluated by the level of spatial thinking, field dependence/field dependence, extraversion, goodwill, consciousness, neurotism, openness of experience, subjectively perceived cognitive load (internal, extraneous and relevant). Research results. The results have shown that the accuracy of the task is moderately positively related to the level of spatial thinking (r=0.37*) and relevant cognitive load (r=0.41*) and negatively with the internal cognitive load (r=–0.42*). In turn, the complexity of the task performed is moderately negatively connected with an extraneous cognitive load (r=–0.33*) and positively with relevant (r=0.32*). At the same time, the greatest contribution to the success of the task is made by an extraneous cognitive load (β=0.72 for accuracy and β=0.48 for complexity). The resulting model describes 49% of the accuracy dispersion and 39% of the dispersions of the complexity of the educational task. It also turned out that personality traits and cognitive style in terms of the parameters of field dependence/field dependence are not associated with the accuracy and complexity of the performance of the specified type of educational task in the VR environment. Conclusions. Data on a significant role of spatial cognitive load is presented useful not only as orientation for further psychological and pedagogical research, but also for the development of practical recommendations to the developers of educational design of virtual simulators for vocational education, as well as specialists in the field of differential pedagogy

The level of cognitive load among students of the College of Engineering and its relationship to some variables

In light of the nature of the methodological study of Engineering departments colleges, which is characterized by difficulty and accumulation of knowledge, the researchers stated that the students in their departments, including the computer department, suffer from difficulty in adapting and adjusting to its subjects, which generates a cognitive burden on them in all its forms as a result of the gap between preparatory school and university lectures, and this is a situation the researchers hypothesis questioning: What is the nature of the relationship between the patterns of cognitive burden and the gender variable to achieve this, the aim of the study is to identify the patterns of the level of cognitive burden among students of the College of Engineering, Computer Department, and then its relationship to the gender variable. Therefore, the importance of this study lies in giving a perception to the teaching staff of the computer department about the cognitive burdens that are generated by their students and thinking about developing solutions through modern teaching strategies. The sample of the study was (60) male and female students from the first stage in the Computer Department / College of Engineering / University of Mosul for the academic year (2022-2021). To achieve the goal of the study and answer its questions, the researchers relied on the cognitive burden scale prepared by Al feel [1] and consisting of (16) paragraphs distributed on three dimensions of the cognitive burden (intrinsic, intrusive, closely related). Using alphacronach equation. After that, the researchers applied the scale to the individuals of the study sample and then analyzed the data statistically "using the ordinal mean, the t-test for two independent samples, and the Point-Passerial correlation coefficient. The results indicated

The level of cognitive load among students of the College of Engineering and its relationship to some variables

In light of the nature of the methodological study of Engineering departments colleges, which is characterized by difficulty and accumulation of knowledge, the researchers stated that the students in their departments, including the computer department, suffer from difficulty in adapting and adjusting to its subjects, which generates a cognitive burden on them in all its forms as a result of the gap between preparatory school and university lectures, and this is a situation the researchers hypothesis questioning: What is the nature of the relationship between the patterns of cognitive burden and the gender variable to achieve this, the aim of the study is to identify the patterns of the level of cognitive burden among students of the College of Engineering, Computer Department, and then its relationship to the gender variable. Therefore, the importance of this study lies in giving a perception to the teaching staff of the computer department about the cognitive burdens that are generated by their students and thinking about developing solutions through modern teaching strategies. The sample of the study was (60) male and female students from the first stage in the Computer Department / College of Engineering / University of Mosul for the academic year (2022-2021). To achieve the goal of the study and answer its questions, the researchers relied on the cognitive burden scale prepared by Al feel [1] and consisting of (16) paragraphs distributed on three dimensions of the cognitive burden (intrinsic, intrusive, closely related). Using alphacronach equation. After that, the researchers applied the scale to the individuals of the study sample and then analyzed the data statistically "using the ordinal mean, the t-test for two independent samples, and the Point-Passerial correlation coefficient. The results indicated 1- The rank level of the total cognitive burden of the three dimensions for male students was (61.60%), female students (61.33%) and the sample as a whole (61.50%), which are medium levels. 2- There are no statistically significant differences at (0.05) level between the averages of the cognitive burden of male and female students in its three dimensions and the total. 3- There is no statistically significant correlation" at (0.05) level between the variables of patterns of cognitive burden as a whole and gender. In light of the results of the study, the researchers came out with a number of conclusions, as well as “presenting a number of recommendations as well as” suggesting a number of titles for

The effect of biomechanical features on classification of dual-task gait.

Early detection of Alzheimer's Disease and Related Disorders (ADRD) has been a focus of research with the hope that early intervention may improve clinical outcomes. The manifestation of motor impairment in early stages of ADRD has led to the inclusion of gait assessments including spatiotemporal parameters in clinical evaluations. This study aims to determine the effect of adding kinetic and kinematic gait features to classification of different levels of cognitive load in healthy individuals. A dual-task paradigm was used to simulate cognitive impairment in 40 healthy adults, with single-task walking trials representing normal, healthy gait. The Paced Auditory Serial Addition Task was administered at two different inter-stimulus intervals representing two levels of cognitive load in dual-task gait. We predicted that a richer dataset would improve classification accuracy relative to spatiotemporal parameters. Repeated Measures ANOVA showed significant changes in 15 different gait features across all three levels of cognitive load. We used three supervised machine learning algorithms to classify data points using a series of different gait feature sets with performance based on the area under the curve (AUC). Classification yielded 0.778 AUC across all three conditions (0.889 AUC Single vs. Dual) using kinematic and spatiotemporal features compared to 0.724 AUC using spatiotemporal features only (0.792 AUC Single vs. Dual). These data suggest that additional kinematic parameters improve classification performance. However, the benefit of measuring a wider set of parameters compared to their cost needs consideration. Further work will lead to a clinically viable ADRD detection classifier.

T-MOOC, cognitive load and performance: analysis of an experience

The study of cognitive load allows us to investigate the effectiveness of any training proposal mediated by technology. This paper presents the results of the implementation of a t-MOOC produced following the DigCompEdu Framework of the European Union. The participants are a group of students (n= 148) from the first year of the Pedagogy Degree (University of Seville) of the Educational Technology subject. To do this, the level of cognitive load invested in the interaction with the t-MOOC is analyzed using a validated scale. Secondly, the relationship between the invested cognitive load and the performance achieved in the experience carried out with the contents of two competence areas is studied. After the different analyzes applied, the study concludes that the t-MOOC produced is considered appropriate for the development of digital skills in students. In addition, although the correlations between cognitive load and academic performance were not very high, both are related. In this sense, the potential of training proposals focused on the development of digital skills and the benefits of applying cognitive load studies are discussed.

Effects of Cognitive Load Level on Students’ Attitude towards the Gamified Course

The aim of this study was to examine the effects of cognitive load level on students’ attitude towards the gamified course. It was also found out the students’ views regarding the 14-week gamified course. Participants consisted of 66 undergraduate students. 40 of them had low cognitive load level and 26 of them had high cognitive load level. According to the results, gamification enabled students to have a positive attitude towards the course, even though they had different cognitive load levels. Furthermore, it was determined a negative and medium significant correlation between the cognitive load and attitude of students with low cognitive load. The gamified course had a positive effect on the “Valuing” and “Positive Effects” sub-dimensions of the attitude in favor of students with low cognitive load. The positive views of many students in qualitative findings strengthened these results. While the gamified course had a negative effect on the "Resisting" sub-dimension in favor of students with high cognitive load, it did not have any significant effect on the "Cost Belief" sub-dimension. A few negative views of students in qualitative findings supported the items in these dimensions of attitude scale. Consequently, this study will strengthen the few studies examining both cognitive and affective effects of a gamified course on students. However, further studies need to confirm these results. For this reason, it is recommended to carry out such studies that reveal all the situations which can affect the attitude in the gamified course in order to achieve positive outcomes of students with different cognitive load levels.

Longitudinal reading outcome and cognitive load in individual- and collaboration-based environments

ABSTRACT In this paper, we report a 12-week longitudinal study aiming at exploring the students’ reading outcome and cognitive load with individual-based print, mobile app of Rain Classroom and collaboration-based social media of WeChat. Administered to 186 postgraduate students in a research university were the weekly reading materials and comprehension tests, together with the self-designed CLCR scale after the reading sessions. A combination of one-way and repeated measures ANOVA along with multi-regression analysis showed that social media produced the best reading outcome; The same pattern occurred about enhanced reading outcome in the initial sessions followed by a decline at the end; Reading outcome in print other than in mobile and social media significantly increased from the first session to the ending session; Reading in mobile generated the highest level of extraneous, intrinsic, and germane load; Multi-regression analysis showed a negative relationship between intrinsic load and the prediction of collaborative reading across all the reading contexts. Further efforts may contribute to more complex patterns of reading outcome and cognitive load status by arranging individual- and collaboration-based contexts in different sequences for students with high and low level of cognitive load.