The objectives of the present study were: a) to investigate three continuous variants of the NASA-Task Load Index (TLX) (standard NASA (CNASA), average NASA (C1NASA) and principal component NASA (PCNASA)) and five different variants of the simplified subjective workload assessment technique (SSWAT) (continuous standard SSWAT (CSSWAT), continuous average SSWAT (C1SSWAT), continuous principal component SSWAT (PCSSWAT), discrete event-based SSWAT (D1SSWAT) and discrete standard SSWAT (DSSWAT)) in terms of their sensitivity and diagnosticity to assess the mental workload associated with agricultural spraying; b) to compare and select the best variants of NASA-TLX and SSWAT for future mental workload research in the agricultural domain. A total of 16 male university students (mean 30.4 +/- 12.5 years) participated in this study. All the participants were trained to drive an agricultural spraying simulator. Sensitivity was assessed by the ability of the scales to report the maximum change in workload ratings due to the change in illumination and difficulty levels. In addition, the factor loading method was used to quantify sensitivity. The diagnosticity was assessed by the ability of the scale to diagnose the change in task levels from single to dual. Among all the variants of NASA-TLX and SSWAT, PCNASA and discrete variants of SSWAT showed the highest sensitivity and diagnosticity. Moreover, among all the variants of NASA and SSWAT, the discrete variants of SSWAT showed the highest sensitivity and diagnosticity but also high between-subject variability. The continuous variants of both scales had relatively low sensitivity and diagnosticity and also low between-subject variability. Hence, when selecting a scale for future mental workload research in the agricultural domain, a researcher should decide what to compromise: 1) between-subject variability or 2) sensitivity and diagnosticity. STATEMENT OF RELEVANCE: The use of subjective workload scales is very popular in mental workload research. The present study investigated the different variants of two popular workload rating scales (i.e. NASA-TLX and SSWAT) in terms of their sensitivity and diagnositicity and selected the best variants of each scale for future mental workload research.
Vacuum-assisted resin transfer molding (VARTM), used in manufacturing medium to large-sized composites for transportation industries, requires non-woven mats. While non-woven glass mats used in these applications are optimized for resin impregnation and properties, such optimized mats for natural fibers are not available. In the current research, cattail fibers were extracted from plants (18–30% yield) using alkali retting and non-woven cattail fiber mat was manufactured. The extracted fibers exhibited a normal distribution in diameter (davg. = 32.1 µm); the modulus and strength varied inversely with diameter, and their average values were 19.1 GPa and 172.3 MPa, respectively. The cattail fiber composites were manufactured using non-woven mats, Stypol polyester resin, VARTM pressure (101 kPa) and compression molding pressures (260 and 560 kPa) and tested. Out-of-plane permeability changed with the fiber volume fraction (Vf) of the mats, which was influenced by areal density, thickness, and fiber packing in the mat. The cattail fibers reinforced the Stypol resin significantly. The modulus and the strength increased with consolidation pressures due to the increase in Vf, with maximum values of 7.4 GPa and 48 MPa, respectively, demonstrating the utility of cattail fibers from waste biomass as reinforcements.
As the Faculty of Engineering at the University of Manitoba begins to emphasize outcome based teaching and assessment along with the traditional input-based teaching and assessment, data are being collected in a variety of forms. Some of the indirect data being gathered comes from students in the form of the Student Exit Survey. This survey was developed to measure students’ perception of how well their program prepared them with regards to the CEAB twelve graduate attributes. The survey asked students to consider a large number of indicators for each of the graduate attributes.The indicator list was originally constructed with the intention of sufficiently defining each attribute for the five engineering programs in the faculty while providing variety and choice. Therefore, the list was fairly extensive, and at times iterative and unwieldy. When revisiting the original Student Exit Survey, two factors ascended in importance: student feedback on their personal attribute competencies as developed within their program, and how to define attribute competency levels.To establish competency levels and make indicators more manageable for faculty and students, the indicators for each attribute were revised to reflect the six levels of Bloom’s Taxonomy of Educational Objectives in the Cognitive Domain: knowledge, comprehension, application, analysis, synthesis and evaluation. This new attribute/indicator format was then developed into theStudent Exit Survey and given to fourth year Mechanical engineering students in Fall 2012. This paper describes that effort and analyzes the initial data from this first pass. This data will be used to inform the continued revision of the Student Exit Survey until it is a reliable and valid instrument for providing feedback at instructor, program and faculty levels as the University of Manitoba’s Faculty of Engineering forges ahead with its continual cycle of improvement.
Drivers' mental workload when using driving assistant systems and in-vehicle automation has been the subject of many studies in recent years. Drivers of semi-autonomous agricultural vehicles are experiencing an increasing number of automated systems. Due to implications of automation support on the operators' performance, a human factors perspective is needed to identify the consequences of such automated systems. In this simulator study, the effects of vehicle steering and implement monitoring and control automation were investigated using a tractor air-seeder system as a case study. Experiments were conducted using the tractor air-seeder driving simulator (TAS-DS) located in the Agricultural Ergonomics Laboratory at the University of Manitoba. Study participants were university students with tractor driving experience. Based on the results from the experiment, most of the automation conditions impose moderate levels of mental workload on operators. Implement monitoring and control automation show significant effect on the drivers' mental workload, contrary to the steering automation. Mental Workload and Driving Task Beside the benefits, automated driving tasks also introduce new problems over the human operator (Stanton and Marsden, 1996). Drivers' mental workload is one of these problems. Mental workload reflects perceptual and cognitive demands of
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