Influence of 2D and 3D view on performance and time estimation in minimal invasive surgery

This study aimed to evaluate the impact of two-dimensional (2D) and three-dimensional (3D) images on time performance and time estimation during a surgical motor task. A total of 60 subjects without any surgical experience (nurses) and 20 expert surgeons performed a fine surgical task with a new laparoscopic technology (da Vinci robotic system). The 80 subjects were divided into two groups, one using 3D view option and the other using 2D view option. We measured time performance and asked subjects to verbally estimate their time performance. Our results showed faster performance in 3D than in 2D view for novice subjects while the performance in 2D and 3D was similar in the expert group. We obtained a significant interaction between time performance and time evaluation: in 2D condition, all subjects accurately estimated their time performance while they overestimated it in the 3D condition. Our results emphasise the role of 3D in improving performance and the contradictory feeling about time evaluation in 2D and 3D. This finding is discussed in regard with the retrospective paradigm and suggests that 2D and 3D images are differently processed and memorised.     

Practice and carryover effects when using small interaction devices

The use of interaction devices in modern work often challenges the human motor system, especially when these devices introduce unfamiliar transformations to the user. In this paper we evaluated expert performance and skill differences between experts and novices when using small motion- and force-controlled interaction devices (touchpad and mini-joystick) in an applied text-editing task. Firstly, experts performed better with their familiar input device than with an unfamiliar one. Particularly touchpad experts operating the unfamiliar mini-joystick showed highly asymmetric carryover costs. Results showed that the efficient performance of experts depended on domain-specific skills, which were not transferable. Secondly, with considerable practice (more than observed for simple and short tasks) novices were brought up to higher levels of performance. The motion-transformation between hand and cursor action was easier in understanding and application than the force-transformation. Thus, the touchpad was used more efficiently than the mini-joystick. In conclusion, practice effects found so far are considerably underestimated when it comes to an applied task. The results give reason to develop and implement skill-sensitive training procedures, since the acquisition of domain-specific skills is critical for expert performance. As a consequence, training procedures might be essential for complex applications and/or unfamiliar device transformations.     

The effect of experience on the test-driven development process

We conducted a quasi-experiment to compare the characteristics of experts’ and novices’ test-driven development processes. Our novices were 11 computers science students who participated in an Extreme Programming lab course, the expert group consisted of seven professionals who had industrial experience in test-driven development. The novices as well as two of the experts worked in a laboratory environment whereas the remaining five experts worked in their office. The experts complied more to the rules of test-driven development and had shorter test-cycles than the novices. The tests written by the experts were of higher quality in terms of statement and block coverage as well. All reported results are statistically significant on the 5% level. We conclude that the results of studies which evaluate performance of test-driven development using subjects inexperienced in TDD are not easily generalisable.     

Skill-based changes in motor performance from attentional focus manipulations: a kinematic analysis

In the present paper, expert and novice law enforcement officers performed a handgun shooting task under varied attention-demanding conditions; outcome (i.e. accuracy, consistency) and movement kinematics were measured (i.e. within and between-trial variability (BTV) of forearm and upper arm absolute angle). Using a dual-task paradigm, we directed participants’ attention towards either a skill-relevant aspect of movement execution or to a skill-irrelevant distractor and compared their data to a single-task control condition. The results showed that experts’ BTV in their upper arm increased during dual-tasks relative to control, but performance was similar across all three conditions. In contrast, novices’ performance was poorer during both dual-tasks relative to control, but limited changes in movement kinematics were observed. This data suggests that attention demanding situations trigger experts’ ability to adapt their movement pattern to maintain end-point control. The data for novices are less clear. Implications for future research are discussed.

Practitioner Summary: Expert and novice law enforcement officials completed a shooting task under baseline and attention-demanding situations. Experts outperformed novices under all conditions, but exhibited increased variability in their upper arm position while shooting during attention-demanding compared to baseline conditions. Novices’ movement data remained variable throughout all conditions. The data suggest that experts are able to maintain shooting performance during an attention-demanding situation by adopting a functional movement strategy.     

What Constitutes Effective Mathematics Instruction: A Comparison of Chinese Expert and Novice Teachers’ Views

Abstract

In this study, we examined the views of 10 experts and 10 novices on effective mathematics instruction in China. We found that all the participants valued students’ mastering of mathematical knowledge and skills and their development in mathematical thinking methods and abilities. Compared with novice teachers, expert teachers emphasized the development of students’ mathematical thinking and higher order thinking abilities and properly dealing with important and difficult content points. In contrast, novice teachers were particularly concerned about the effectiveness of teachers' guidance.     

Biomechanical differences between expert and novice workers in a manual material handling task

The objective was to verify whether the methods were safer and more efficient when used by expert handlers than by novice handlers. Altogether, 15 expert and 15 novice handlers were recruited. Their task was to transfer four boxes from a conveyor to a hand trolley. Different characteristics of the load and lifting heights were modified to achieve a larger variety of methods by the participants. The results show that the net moments at the L5/S1 joint were not significantly different (p > 0.05) for the two groups. However, compared with the novices, the experts bent their lumbar region less (experts 54° (SD 11°); novices 66° (SD 15°)) but bent their knees more (experts approx. 72° (SD approx. 30°); novices approx. 53° (SD approx. 33°), which brought them closer to the box. The handler's posture therefore seems to be a major aspect that should be paid specific attention, mainly when there is maximum back loading.

Statement of Relevance: The findings of this research will be useful for improving manual material handling training programmes. Most biomechanical research is based on novice workers and adding information about the approach used by expert handlers in performing their tasks will help provide new avenues for reducing the risk of injury caused by this demanding physical task.     

Quantitative and qualitative differences between experts and novices in chunking computer software knowledge

This study investigated quantitative and qualitative differences between experts and novices in knowledge structure, specifically in their chunking of computer software knowledge. An experiment was conducted to collect pairwise relatedness ratings of 23 concepts in C computer programming from 10 experts and 10 novices. Correlation analyses of relatedness ratings were performed to examine expert‐novice differences in overall knowledge structure. The intragroup correlation coefficients of experts were greater than the intragroup correlation coefficients of novices and the intergroup correlation coefficients between experts and novices. Hence, there existed structural differences of knowledge between experts and novices. To examine how experts differ from novices quantitatively and qualitatively in their knowledge chunking, the relatedness ratings of experts were averaged within the group to give a single set of relatedness ratings, and the relatedness ratings of novices were also averaged within the group to give a single set of relatedness ratings. Then the hierarchical clustering analyses were performed on the set of relatedness ratings for the experts group and on the set of relatedness ratings for the novices group, respectively. The hierarchical clustering of C concepts made by experts and the hierarchical clustering of C concepts made by novices indicated that novices had more but smaller knowledge chunks than experts for the common set of knowledge, and thatthe rough chunking of knowledge made by novices was qualitatively similar to the rough chunking of knowledge made by experts, although on a more detailed level qualitative differences still existed.     

Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction

In order to accomplish diverse tasks successfully in a dynamic (i.e., changing over time) construction environment, robots should be able to prioritize assigned tasks to optimize their performance in a given state. Recently, a deep reinforcement learning (DRL) approach has shown potential for addressing such adaptive task allocation. It remains unanswered, however, whether or not DRL can address adaptive task allocation problems in dynamic robotic construction environments. In this paper, we developed and tested a digital twin-driven DRL learning method to explore the potential of DRL for adaptive task allocation in robotic construction environments. Specifically, the digital twin synthesizes sensory data from physical assets and is used to simulate a variety of dynamic robotic construction site conditions within which a DRL agent can interact. As a result, the agent can learn an adaptive task allocation strategy that increases project performance. We tested this method with a case project in which a virtual robotic construction project (i.e., interlocking concrete bricks are delivered and assembled by robots) was digitally twinned for DRL training and testing. Results indicated that the DRL model’s task allocation approach reduced construction time by 36% in three dynamic testing environments when compared to a rule-based imperative model. The proposed DRL learning method promises to be an effective tool for adaptive task allocation in dynamic robotic construction environments. Such an adaptive task allocation method can help construction robots cope with uncertainties and can ultimately improve construction project performance by efficiently prioritizing assigned tasks.     

Interacting with notebook input devices: an analysis of motor performance and users' expertise

In the present study the usability of two different types of notebook input devices was examined. The independent variables were input device (touchpad vs. mini-joystick) and user expertise (expert vs. novice state). There were 30 participants, of whom 15 were touchpad experts and the other 15 were mini-joystick experts. The experimental tasks were a point-click task (Experiment 1) and a point-drag-drop task (Experiment 2). Dependent variables were the time and accuracy of cursor control. To assess carryover effects, we had the participants complete both experiments, using not only the input device for which they were experts but also the device for which they were novices. Results showed the touchpad performance to be clearly superior to mini-joystick performance. Overall, experts showed better performance than did novices. The significant interaction of input device and expertise showed that the use of an unknown device is difficult, but only for touchpad experts, who were remarkably slower and less accurate when using a mini-joystick. Actual and potential applications of this research include an evaluation of current notebook input devices. The outcomes allow ergonomic guidelines to be derived for optimized usage and design of the mini-joystick and touchpad devices.     

Learning to tie well with others: bimanual versus intermanual performance of a highly practised skill

Studies indicate that novices are faster in manual tasks when performing with a partner (‘intermanual’) than with their own two hands (‘bimanual’). The generality of this ‘mode effect’ was examined using a highly practised bimanual task, shoe tying, at which participants were experts. Speed–variability correlations confirmed participants were bimanually skilled but not intermanually skilled. Contrary to results using novices, intermanual was slower, such that prior skill reverses the effect. Analyses incorporating the similarity of shoe-tying strategies across dyads implicated a perceptual rather than shared knowledge/representation basis for intermanual performance. Practice effects indicated that intermanual performance built upon prior bimanual skill, such that novel relative timings between dyads' hands must be acquired. Motor transfer effects provided support for this conclusion. During shoe tying, hands were tightly coupled in the intermanual mode due to the perceptual coupling constraints of intermanual performance. Increased coupling was correlated with slower performance. Implications for real-world tasks (e.g. surgical knot tying) are described.     

The influence of user expertise and prototype fidelity in usability tests

An empirical study examined the impact of user expertise and prototype fidelity on the outcomes of a usability test. User expertise (expert vs. novice) and prototype fidelity (paper prototype, 3D mock-up, and fully operational appliance) were manipulated as independent variables in a 2 × 3 between-subjects design. Employing a floor scrubber as a model product, 48 users carried out several cleaning tasks. Usability problems identified by participants were recorded. Furthermore, performance, system management strategies and perceived usability were measured. The results showed that experts reported more usability problems than novices but these were considered to be less severe than those reported by novices. Reduced fidelity prototypes were generally suitable to predict product usability of the real appliance. The implications for the running of usability tests are specific to the fidelity of the prototype.     

Information problem solving by experts and novices: analysis of a complex cognitive skill

In (higher) education students are often faced with information problems: tasks or assignments that require them to identify information needs, locate corresponding information sources, extract and organize relevant information from each source, and synthesize information from a variety of sources. It is often assumed that students master this complex cognitive skill of information problem solving all by themselves. In our point of view, however, explicit and intensive instruction is necessary. A skill decomposition is needed in order to design instruction that fosters the development of information problem solving. This research analyzes the information problem solving process of novices and experts in order to reach a detailed skill decomposition. Results reveal that experts spend more time on the main skill ‘define problem’ and more often activate their prior knowledge, elaborate on the content, and regulate their process. Furthermore, experts and novices show little differences in the way they search the Internet. These findings formed the basis for formulating instructional guidelines.     

An Effective Framework for Fast Expert Mining in Collaboration Networks: A Group-Oriented and Cost-Based Method

The growth of social networks in modern information systems has enabled the collaboration of experts at a scale that was unseen before.Given a task and a graph of experts where each expert possesses some skills,we tend to find an effective team of experts who are able to accomplish the task.This team should consider how team members collaborate in an effective manner to perform the task as well as how efficient the team assignment is,considering each expert has the minimum required level of skill.Here,we generalize the problem in multiple perspectives.First,a method is provided to determine the skill level of each expert based on his/her skill and collaboration among neighbors.Second,the graph is aggregated to the set of skilled expert groups that are strongly correlated based on their skills as well as the best connection among them.By considering the groups,search space is significantly reduced and moreover it causes to prevent from the growth of redundant communication costs and team cardinality while assigning the team members.Third,the existing RarestFirst algorithm is extended to more generalized version,and finally the cost definition is customized to improve the efficiency of selected team.Experiments on DBLP co-authorship graph show that in terms of efficiency and effectiveness,our proposed framework is achieved well in practice.     

Computerized adaptive tutorials to improve and assess problem-solving skills

Recent research studies have reported on how novices and experts differ in storing, organizing, and retrieving subject matter knowledge and on how they apply their knowledge to solve new problems. These findings have been integrated and implemented in a computer environment to help novices improve their problem-solving skills. An outline of this implementation and the manner in which it is used in an undergraduate hydraulic engineering course are presented. Student feedback on the educational value of this approach as well as data to demonstrate the improvement in their problem-solving abilities are also included. Preliminary evidence supporting the validity of computerized assessment of problem-solving skills is also included.     

A Design Thinking Rationality Framework: Framing and Solving Design Problems in Early Concept Generation

The concept of “Design Thinking” opens up debate regarding the prevalent human–computer interaction design practice. This article focuses specifically on the cognitive processes of designers during their early design activities. Two groups of designers—experts and novices—were asked to develop a fictitious vacuum cleaner. We then examined the different ways in which these groups manage their design thinking processes and how the groups choose design concepts. The empirical study revealed that expert designers are effective at framing design problems. They make quick decisions (through the use of the affect heuristic) but are more wedded to their own previously developed design concepts, which they do not change in subsequent design stages. In contrast, novice designers are less skilled in framing new design problems but better able to renounce their initial design concepts. These diverse design thinking approaches are linked to potential problems. We then discuss how to address these concerns in conjunction with empathy for the artifact (i.e., artifact empathy via the mediated self) or user (i.e., user empathy via the simulated self), problem framing with second-order semantic connotations, and irrationality when analyzing design solutions. Finally, we propose a design thinking rationality framework that can establish a designer's view of design activities and thereby assist designers educated in both creative and rational design decisions.     

Some surprising differences between novice and expert errors in computerized office work

This paper investigates the impact of different levels of expertise on errors in human-computer interaction. In a field study 174 clerical workers from 12 different companies were observed during their normal office work and were questioned on their expertise with computers. The level of expertise was determined by (a) the length of time an employee had worked with a computer (computer expertise); (b) the number of programs she knew (program expertise); and (c) the daily time s/he spent working with the computer (daily work-time expertise). These different operationalizations of novices and experts led to different results. In contrast to widespread assumptions, experts did not make fewer errors than novices (except in knowledge errors). On the other hand, experts spent less time handling the errors than novices. A cluster analysis produced four groups in the workforce: occasional users, frequent users, beginners, and general users.     

Cognitive analysis of experts’ and novices’ concept mapping processes: An eye tracking study

The goal of this study was to explore how designated experts (subject experts with extensive experience in science education and concept mapping) and novices (pre-service teachers) establish concept map (CM) development processes while considering their cognitive processes. Two experiments were conducted in which eye-tracking, written, and verbal data were collected from 29 novices and 6 subject matter experts. The results showed that despite some similar strategies, novices and experts followed different patterns during the CM development process. Both experts and novices embraced deductive reasoning and preferred hierarchical type CMs. Additional points studied during the process include filling out requested information in different orders, branch construction pattern, content richness and progress pattern. Furthermore, eye behavior patterns also differed among experts and novices. Expert participants differed from novices in all eye behavior metrics (fixation count, fixation and visit duration for specific actions). Novices’ fixation count (FC) numbers were higher than the experts’ during the entire process and in specific periods. In conclusion, these pattern differences affect the CM development process directly. Considering the patterns revealed in the study may help instructors guide learners more adequately and effectively.     

Some surprising differences between novice and expert errors in computerized office work

Abstract

This paper investigates the impact of different levels of expertise on errors in human-computer interaction. In a field study 174 clerical workers from 12 different companies were observed during their normal office work and were questioned on their expertise with computers. The level of expertise was determined by (a) the length of time an employee had worked with a computer (computer expertise); (b) the number of programs she knew (program expertise); and (c) the daily time s/he spent working with the computer (daily work-time expertise). These different operationalizations of novices and experts led to different results. In contrast to widespread assumptions, experts did not make fewer errors than novices (except in knowledge errors). On the other hand, experts spent less time handling the errors than novices. A cluster analysis produced four groups in the workforce: occasional users, frequent users, beginners, and general users.     

3D Object retrieval based on viewpoint segmentation

In the last decades, extensive efforts have been dedicated to develop better 3D object retrieval methods. View-based methods have attracted a significant amount of attention, not only because of their state-of-the-art performance, but also they merely require some of a 3D object’s 2D view images. However, most recent approaches only deal with the images’ content difference without the discrepancy of view relative positions. In this paper, we propose a normal method for view segmentation, based on Markov random field (MRF) model, which consider not only the difference between the content of views but also the relative locations. Each view is obtained by projecting at certain viewpoints and angels, therefore, these locations can be applied to depict each view, with content of views. We use the MRF to implement view segmentation and choose the representative views. Finally, we present a framework based on the proposed view segmentation method for 3D object retrieval and the experimental results demonstrate that the proposed method can achieve better retrieval effectiveness than state-of-the-art methods under several standard evaluation measures.