Low Performers Research Articles

COVID-19 case fatality rate in the context of healthcare system organization and EHCI performance: Focus on the Visegrad (V4) countries

Abstract This article tries to explain the differences in COVID-19 case fatality rate (CFR) in 22 European countries by their type of organization and performance level of their healthcare systems. The CFR is taken here as the most important indicator since it measures the ratio between COVID deaths and COVID cases. In our view, this indicator reflects the true performance of the healthcare system, as this indicator is freed form public health interventions, like testing, lockdowns or social distancing. Our research is also unique, because it sees the healthcare system in a holistic way and tries to explain the CFR not by individual risk factors, socioeconomic indicators, or partial system parameters, but by using a complex healthcare system classification method adopted from Isabelle Joumard and an overall healthcare system performance index adopted from European Health Consumer Index (EHCI). The main results are twofold. First, higher EHCI score is related to lower CFR. So, the countries are cumulated basically in two quadrants: High EHCI performers (score 790 and higher) with low CFR (below 1.93%) and low EHCI performers with high COVID CFR. Second, apart from Czech Republic, the V4 countries are not doing very well in fighting COVID. Hungary is the worst, not only from the V4 group, but the worst from the whole list of 22 European countries included in this research. Poland is doing better, but still is high above the median CFR. Slovakia was the second worst from the V4 group. Czech Republic is the best V4 performer and the only country with EHCI score lower than median and CFR also lower than the median.

Practice Effects of Mobile Tests of Cognition, Dexterity, and Mobility on Patients With Multiple Sclerosis: Data Analysis of a Smartphone-Based Observational Study.

BackgroundSmartphones and their built-in sensors allow for measuring functions in disease-related domains through mobile tests. This could improve disease characterization and monitoring, and could potentially support treatment decisions for multiple sclerosis (MS), a multifaceted chronic neurological disease with highly variable clinical manifestations. Practice effects can complicate the interpretation of both improvement over time by potentially exaggerating treatment effects and stability by masking deterioration.ObjectiveThe aim of this study is to identify short-term learning and long-term practice effects in 6 active tests for cognition, dexterity, and mobility in user-scheduled, high-frequency smartphone-based testing.MethodsWe analyzed data from 264 people with self-declared MS with a minimum of 5 weeks of follow-up and at least 5 repetitions per test in the Floodlight Open study, a self-enrollment study accessible by smartphone owners from 16 countries. The collected data are openly available to scientists. Using regression and bounded growth mixed models, we characterized practice effects for the following tests: electronic Symbol Digit Modalities Test (e-SDMT) for cognition; Finger Pinching and Draw a Shape for dexterity; and Two Minute Walk, U-Turn, and Static Balance for mobility.ResultsStrong practice effects were found for e-SDMT (n=4824 trials), Finger Pinching (n=19,650), and Draw a Shape (n=19,019) with modeled boundary improvements of 40.8% (39.9%-41.6%), 86.2% (83.6%-88.7%), and 23.1% (20.9%-25.2%) over baseline, respectively. Half of the practice effect was reached after 11 repetitions for e-SDMT, 28 repetitions for Finger Pinching, and 17 repetitions for Draw a Shape; 90% was reached after 35, 94, and 56 repetitions, respectively. Although baseline performance levels were highly variable across participants, no significant differences between the short-term learning effects in low performers (5th and 25th percentile), median performers, and high performers (75th and 95th percentile) were found for e-SDMT up to the fifth trial (β=1.50-2.00). Only small differences were observed for Finger Pinching (β=1.25-2.5). For U-Turn (n=15,051) and Static Balance (n=16,797), only short-term learning effects could be observed, which ceased after a maximum of 5 trials. For Two Minute Walk (n=14,393), neither short-term learning nor long-term practice effects were observed.ConclusionsSmartphone-based tests are promising for monitoring the disease trajectories of MS and other chronic neurological diseases. Our findings suggest that strong long-term practice effects in cognitive and dexterity functions have to be accounted for to identify disease-related changes in these domains, especially in the context of personalized health and in studies without a comparator arm. In contrast, changes in mobility may be more easily interpreted because of the absence of long-term practice effects, even though short-term learning effects might have to be considered.

An injection optimization decision-making tool using streamline based fuzzy logic workflow

This paper presents an adaptive approach to optimize field injection strategies using streamline-based well allocations coupled with fuzzy logic. The strength of our approach comes from the fact that streamlines are generated by running a full-physics reservoir simulator. Streamlines provide great insights about well pattern connectivity and good allocation factors allowing the injection efficiency (IE) for each pattern to be determined. Fuzzy logic can simulate human thinking and handle different categories of information including linguistic, imprecise, approximate, and overlapping to name a few. This paper presents a genuine approach for field injection optimization using a streamlined-based fuzzy logic system. In this work, we present an adaptive streamline-based fuzzy logic system that uses three input parameters namely injection efficiency (IE), water cut (WC), and injection loss to aquifer to assign an injector ranking index (IRI) according to injector performances. The workflow then smartly redistributes water injection by accounting for operational constraints and number of connected producers in a pattern in addition to the IRI. The workflow examines the low performers (i.e., low and medium IRI categories) and assigns different injection reduction factors for each injector in these categories. Then, the total amount of reduced injection is assigned to high performers (i.e., high IRI) while ensuring no operational constraint is violated, such as bottom-hole pressure (BHP) and capacity of pumps. This approach has been tested on a dual-porosity dual-permeability (DPDP) conceptual simulation model. The area of interest has two rows of injectors: downdip and updip. The updip injectors are the focus of the study. The results of applying this approach show noticeable improvements in injection efficiency for most wells in the area of interest ensuring better sweep, good pressure support, and improving cumulative oil production. We believe combining both technologies, namely streamlines and fuzzy logic, can provide reservoir engineers with a robust decision-making tool to attain a more successful field-wide water injection strategy.

Helping higher education students succeed: An examination of student attributes and academic grade performance

Academic grade performance can be influenced by individual attributes, including motivation, attitudes, and beliefs. These attributes can be manifested by current events, such as technology or world events. Through a survey-based study using validated measures coupled with student grade performance at three instances during their tenure in higher education, we document that attributes common to current students influence academic grade performance differently for higher versus lower performers. Among other findings, we identify that higher performing students’ grade performance is positively affected by the attributes: fear of punishment and distributive justice; while lower performing students’ grades were not affected by those attributes. We provide practical recommendations for faculty to help influence attributes in order to potentially improve academic grade performance. Indeed, this study has important implications for those involved in fostering student success.

Got Stage Fright? A Fellow-Led Initiative to Improve Cancer Staging Documentation

INTRODUCTION Accurate cancer staging is essential for determining prognosis and treatment. However, only 6.1% of new patient visits (NPV) at our Comprehensive Cancer Center (CCC) were staged at baseline using our electronic health record staging tool (EST). We used Quality Improvement (QI) methods, targeting oncology fellows, to improve fellow-seen NPV staging rates by the end of the 2018-19 academic year. METHODS We aimed to increase fellow monthly EST usage rate for solid tumor NPVs to 70% at our Cancer Center by June 2019. In our first Plan-Do-Study-Act (PDSA) cycle, we moved our EST into the visit navigator that unifies all components needed to complete an encounter. In our second PDSA cycle, we specifically engaged fellows with monthly reminder emails, periodic surveys, and individualized outreach to low performers. We tracked fellow-seen NPV staging rates, and, using statistical process control charting, CCC-wide NPV staging rates. RESULTS Monthly fellow-seen NPV staging rose from 47% in July 2018 to a mean of 75% during PDSA 1 and 70% during PDSA 2. CCC-wide NPV staging rose from 6.1% to 16% during PDSA 1 and 13% during PDSA 2. Staging rates decreased with the arrival of new fellows. CONCLUSIONS Our initiative improved timely staging rates for fellow-seen NPVs. Although fellows see only a minority of NPVs at our CCC, we found that our fellow-oriented QI initiative appeared to improve CCC-wide NPV staging rates as well (in the absence of any comparable initiatives). Future steps include the development of note templates with embedded staging tools to promote sustainability.

Cognitive Patterns and Conversion in a Representative Sample of Individuals at Risk for Psychosis.

Clinical high-risk (CHR) individuals belong to a heterogeneous group, of which only a few will cross the threshold for a clinical diagnosis. Cognitive disturbances are present in CHR subjects and may be indicative of transition. Our study aims to identify such deficits in a representative CHR for psychosis sample. Our sample comprised 92 CHR individuals and 54 controls from a representative cohort of the general population. They were followed up for a mean of 2.5 years, with 15 individuals converting to schizophrenia or other Diagnostic and Statistical Manual of Mental Disorders, 5th Edition diagnoses. Neurocognitive assessment was performed with the University of Pennsylvania Computerized Neuropsychological Testing, and CHR status was assessed with the Structured Interview for Prodromal Syndromes (SIPS). Baseline scores were entered in a latent profile analysis model. Our study brought forward a four-class model on cognitive performance. One class displayed better performance, whereas the other three performed worse, all compared with controls. The class with lower executive function also had the highest score on disorganized communication (SIPS P5 = 1.36, p < 0.05), although unrelated to conversion. Among the low performers, the class significantly related to conversion (p = 0.023) had the highest score in decreased expression of emotion (SIPS N3 = 0.85, p < 0.05). Our study brings new and relevant data on non-help-seeking CHR individuals and the relationship between cognitive patterns and conversion. We have highlighted a specific cognitive signature, associated with negative symptoms, which represents a stable trait with presumed lower conversion to a psychiatric illness.

Option to cooperate increases women's competitiveness and closes the gender gap

We advance the hypothesis that women are as competitive as men once the incentive for winning includes factors that matter to women. Allowing winners an opportunity to share some of their winnings with the low performers has gendered consequences for competitive behavior. We ground our work in an evolutionary framework in which winning competitions brings asymmetric benefits and costs to men and women. In the new environment, the potential to share some of the rewards from competition with others may afford women the benefit of reaping competitive gains without incurring some of its potential costs. An experiment ( N = 438 in an online convenience sample of U.S. adults) supports our hypothesis: a 26% gender gap in performance vanishes once a sharing option is included to an otherwise identical winner-take-all incentive scheme. Besides providing a novel experiment that challenges the paradigm that women are not as motivated to compete as men, our work proposes some suggestions for policy: including socially-oriented rewards to contracts may offer a novel tool to close the persistent labor market gender gap. • The hypothesis that women have a lower desire to compete than men is investigated through an evolutionary framework. • Idea embraced as an explanation for why women are a minority in high-ranking economic and political positions. • Different sexes evolved to pursue different competitive strategies, females focusing competitiveness for offspring benefit. • Experiment (N = 438 Mturk adults) supports hypothesis that women compete as much as men with prosocial option. • Result suggests important implications for designing policies to promote gender equality: change the system not the women.

Tracking strategy changes using machine learning classifiers.

In complex tasks, high performers often have better strategies than low performers, even with similar amounts of practice. Relatively little research has examined how people form and change strategies in tasks that permit a large set of strategies. One challenge with such research is identifying strategies based on behavior. Three algorithms were developed that track the task features people use in their strategies while performing a complex task. Two of these algorithms were based on task-general, machine-learning classifiers: a support vector machine and a decision tree algorithm. The third was a task-specific algorithm. Data from several strategies in a complex task were simulated, and the algorithms were tested to see how well they identified the underlying features of the simulated strategy. The two machine-learning classifiers performed better than the task-specific algorithm. However, the two classifiers differed on how well they identified different types of strategies. The first two studies show that the ability of these algorithms to recover the underlying strategy depends on the complexity of the strategy relative to the quantity of performance data available. If the underlying strategy changes too frequently, then the performance of the algorithms suffers. However, results from the third study show that it is possible to use these algorithms to track strategy changes that occur in a task. The fourth study examines performance on data from human participants. This approach to tracking strategy exploration may enable further development of theories about how people search for and select effective strategies.

Correlates of Balance and Aerobic Indices in Lower-Limb Prostheses Users on Arm Crank Exercise.

Background: The HUMAC Balance System (HBS) offers valid measurement of balance, and the arm crank exercise test (ACE) is a valid measure of physiological capacity. Neither have been used to evaluate associations between balance and physiological capacity in lower-limb amputees. Methods: Thirty-five participants with lower-limb amputations were recruited. Standing balance (center of pressure) was evaluated during eyes opened (EO) and eyes closed (EC) conditions using the HBS. Participants performed ACE graded exercise testing (GXT) to evaluate aerobic capacity. Spearman’s rho was used to identify relationships between variables. Cut-points for three groups were generated for time on ACE. Mann–Whitney U tests were used to explore significant differences in variables of balance and ACE between low and high performers. Results: Relationships between variables of eyes open displacement (EOD), eyes open velocity (EOV), eyes closed displacement (ECD), and eyes closed velocity (ECV) were significant (p < 0.05), and high performers with EO also performed best with EC. Longer exercise times were significantly associated with increased HRpeak, VO2peak, VEpeak, and RERpeak (p < 0.05). HRpeak (143.0 ± 30.6 b/min), VO2peak (22.7 ± 7.9 and 10.6 ± 4.7 mL/kg/min), VEpeak (80.2 ± 22.2 and 33.2 ± 12.7 L/min), and RERpeak (1.26 ± 0.08 and 1.13 ± 0.11) were significantly greater in high performers than low performers, respectively (p < 0.05). There was no significant association among VO2peak and any balance task variables; however, there were significant associations between some balance and physiological variables. Conclusions: Findings differentiated high and low performers; however, participants were still well below able-bodied norms of physical capacity. Training to mitigate deconditioning is suggested.

Community Health Centers’ Performance in Cancer Screening and Prevention

Little is known about what clinic-level factors differentiate community health centers that achieve high performance on cancer-preventive care metrics. This study aims to describe the longitudinal trends in the delivery of 3 cancer-preventive care metrics (cervical and colorectal cancer screenings and tobacco-cessation intervention) and define and compare community health centers with high cancer-preventive care performance with those with low cancer-preventive care performance. This observational study used 2012-2019 community health center data (N=933) from the Uniform Data System. High/low performance was based on Healthy People 2020 targets and sample distribution. For each cancer-preventive care metric, the percentage of community health centers that met high (≥70.5% at cervical or colorectal cancer screening or >80% tobacco-cessation intervention) and low thresholds at 1, 2, and all the 3 screenings was estimated. Multivariable generalized estimating equations logistic regression modeling was used to assess the community health center‒level factors associated with screening performance. The community health centers' performance for tobacco-cessation intervention remained at ≥80%, with a small increase over time. Performance for cervical cancer screening remained unchanged with about 50% of patients screened. Colorectal cancer screening performance increased from around 30% in 2012 to 44% in 2019. Very few community health centers reached high performance (3%) in all the 3 indicators, and 13% of community health centers were high in any 2 of the outcomes in 2019. Higher patient volume, a greater proportion of Hispanic patients, fewer uninsured patients, and community health centers located in the Northeast region were associated with high performance in 2019. Very few community health centers meet all Healthy People 2020 goals in cancer screenings and may struggle to achieve the 2030 goals. Very few indicators differentiated high performers from low performers.

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces.

Motor Imagery BCI systems have a high rate of users that are not capable of modulating their brain activity accurately enough to communicate with the system. Several studies have identified psychological, cognitive, and neurophysiological measures that might explain this MI-BCI inefficiency. Traditional research had focused on mu suppression in the sensorimotor area in order to classify imagery, but this does not reflect the true dynamics that underlie motor imagery. Functional connectivity reflects the interaction between brain regions during the MI task and resting-state network and is a promising tool in improving MI-BCI classification. In this study, 54 novice MI-BCI users were split into two groups based on their accuracy and their functional connectivity was compared in three network scales (Global, Large and Local scale) during the resting-state, left vs. right-hand motor imagery task, and the transition between the two phases. Our comparison of High and Low BCI performers showed that in the alpha band, functional connectivity in the right hemisphere was increased in High compared to Low aptitude MI-BCI users during motor imagery. These findings contribute to the existing literature that indeed connectivity might be a valuable feature in MI-BCI classification and in solving the MI-BCI inefficiency problem.

Building bridges: the impact of scaffolds in PBL on the learning of South African pre-service geography teachers

ABSTRACT This study explores pre-service geography teachers’ assessment of the impact of hard and soft scaffolds used in problem-based learning on their own learning. Respondents had to complete a “Scaffolds in geography problem-based learning” instrument after the problem-based learning intervention. The respondents could also elaborate on the impact of the scaffolds on their learning in the comments section of the instrument. The results indicated that most of the respondents perceived that all the scaffolds used in the problem-based learning activities had a positive impact on their learning. The soft scaffolds were perceived to have a higher impact on their learning compared to the hard scaffolds. Most of the respondents highlighted in their comments the important impact of the facilitator and group members on their learning. In addition, they indicated the important role of the training video and workshop on problem-based learning as examples of hard scaffolds to give them a good understanding of problem-based learning. No gender differences in the respondents’ perceptions of the impact of scaffolds in problem-based intervention on their learning were reported. Respondents with high academic performance in geography rated the impact of the scaffolds on their learning to be larger compared to the lower academic performers.

A Test of a Configurational Model of Agency Performance in the United States Federal Government Using Machine Learning Methodology

ABSTRACT This paper takes PA research on organizational performance in a new direction by testing a configurational model using self-organizing maps, a machine learning methodology. The model was built and tested using six performance dimensions from 2017 Federal Employee Viewpoint Survey (FEVS). Four distinct performance profiles or groups were identified: very low performers, average performers, transitional performers, and high performers. Implications for theory development and practice of configurational models of public organizational performance were discussed.

INDIAN BANKING INDUSTRY: A NEW EVIDENCE FROM A PROFITABILITY PERSPECTIVE

While India is set to become the world’s most populous country by 2050, it is also the home to the world’s largest number of unbanked individuals. This paper aims to investigate the profitability issue with a focus on public banks. Using a new methodology based on comparisons tests and panel analysis that test unobserved heterogeneities between banks. We show that public banks are not low performers, nor can private banks be considered high performers Finally, we show that the proportion of non-performing assets (NPAs) is a real concern and requires urgent attention of government and regulators for Indian banks to serve profitability their home market. Banks that make more profits on non-interest income are not necessarily less profitable than others. Further, outcomes favour the ideas that if public banks are able to clean-up their non-performing assets as well as follow a sound prudential regulation, their profits could strongly grow. Future reforms must consider the public bank’s key role in the growth of the India’s economic outlook, especially when it comes to projects of social importance and national priority. The study is based on 105 banks with cross-sections from 2003–2016; however, India’s government has initiated reforms in the banking segment, which has led to a significant decrease in government stake and the number of banks.

Evaluating Education Governance: Does State Takeover of School Districts Affect Student Achievement?

AbstractLocal school boards have primary authority for running educational systems in the U.S., but little is known empirically about the merits of this arrangement. State takeovers of struggling districts represent a rare alternative form of educational governance and have become an increasingly common response to low performance. However, limited research explores whether this effectively improves student outcomes. We track all takeovers nationwide from the late 1980s, when the first takeovers occurred, through 2016 and describe takeover districts. While these districts are low performing, we find academic performance plays less of a role in predicting takeover for districts serving larger concentrations of African American students. We then use a new data source allowing for cross‐state comparisons of student outcomes to estimate the effect of takeovers that occurred between 2011 and 2016. On average, we find no evidence that takeover generates academic benefits. Takeover appears to be disruptive in the early years of takeover, particularly to English Language Arts achievement, although the longer‐term effects are less clear. We also observe considerable heterogeneity of effects across districts. Takeovers were least effective in districts with higher baseline achievement and least harmful in majority‐Hispanic communities. Leaders should be cautious about using state takeover without considering local context and a better understanding of why some takeovers are more effective than others.

Microbial taxa in dust and excreta associated with the productive performance of commercial meat chicken flocks

BackgroundA major focus of research on the gut microbiota of poultry has been to define signatures of a healthy gut and identify microbiota components that correlate with feed conversion. However, there is a high variation in individual gut microbiota profiles and their association with performance. Population level samples such as dust and pooled excreta could be useful to investigate bacterial signatures associated with productivity at the flock-level. This study was designed to investigate the bacterial signatures of high and low-performing commercial meat chicken farms in dust and pooled excreta samples. Poultry house dust and fresh pooled excreta were collected at days 7, 14, 21, 28 and 35 of age from 8 farms of two Australian integrator companies and 389 samples assessed by 16S ribosomal RNA gene amplicon sequencing. The farms were ranked as low (n = 4) or high performers (n = 4) based on feed conversion rate corrected by body weight.ResultsPermutational analysis of variance based on Bray–Curtis dissimilarities using abundance data for bacterial community structure results showed that company explained the highest variation in the bacterial community structure in excreta (R2 = 0.21, p = 0.001) while age explained the highest variation in the bacterial community structure in dust (R2 = 0.13, p = 0.001). Farm performance explained the least variation in the bacterial community structure in both dust (R2 = 0.03, p = 0.001) and excreta (R2 = 0.01, p = 0.001) samples. However, specific bacterial taxa were found to be associated with high and low performance in both dust and excreta. The bacteria taxa associated with high-performing farms in dust or excreta found in this study were Enterococcus and Candidatus Arthromitus whereas bacterial taxa associated with low-performing farms included Nocardia, Lapillococcus, Brachybacterium, Ruania, Dietzia, Brevibacterium, Jeotgalicoccus, Corynebacterium and Aerococcus.ConclusionsDust and excreta could be useful for investigating bacterial signatures associated with high and low performance in commercial poultry farms. Further studies on a larger number of farms are needed to determine if the bacterial signatures found in this study are reproducible.

The effects of performance evaluation on punishment in organisations

This article expands understanding of the relationship between punishment and performance in organisations. We analyse Korean data on performance and punishment as follows. While prior literature has focused on the effect of punishment on performance, this article examines the effect of performance gaps on punishment. Based on behavioural theory, strategic reference point theory and principal–agent theory, it sheds light on the negative relationship between performance gaps (cause) and punishment (effect). The article demonstrates that low performers are more often punished and high performers less so (i.e. a negativity bias). Our results also show that organisations prioritise light punishment over heavy punishment when disciplining employees in response to performance. Points for practitioners Low-performing organisations are more often punished and high-performing organisations less often so. The degree to which negative performance increases punishment is greater than that to which positive performance decreases punishment. Organisations prioritise light punishment over heavy punishment when disciplining employees in response to performance.

Family Medicine Clerkship Assessment Analysis: Application and Implications.

Clerkship assessment structures should consist of a systematic process that includes information from exam and assignment data to legitimize student grades and achievement. Analyzing student performance across assessments, rather than on a single assignment, provides a more accurate picture to identify academically at-risk students. This paper presents the development and implications of a structured approach to assessment analysis for the Family Medicine Clerkship at Florida International University Herbert Wertheim College of Medicine. The assessment analysis included a table presenting the distribution of all assessment performance results for 166 clerkship students from April 2018 to June 2019. A correlation table showed linear relationships between performance on all graded activities. We conducted a Pearson analysis (r), coefficient of determination (r 2), multiple regression analysis, and reliability of performance analysis. Performance on one assessment-the core skills quiz-yielded a statistically significant correlation (r=.409, r 2= .16, P<.001) with the final clerkship grade. The reliability of performance analyses showed low performers (<-1.7 SD), had both a low mean quiz score (59.6) and final grade (83). Top performers (>-1.7 SD) had both a high mean quiz score (88.5) and final grade (99.6). This was confirmed by multiple regression analysis. The assessment analysis revealed a strong linear relationship between the core skills quiz and final grade; this relationship did not exist for other assignments. In response to the assessment analysis, the clerkship adjusted the grading weight of its assignments to reflect their utility in differentiating academic performance and implemented faculty development regarding grading for multiple assignments.

Exploring students' cognitive and affective states during problem solving through multimodal data: Lessons learned from a programming activity

AbstractBackgroundProblem‐solving is a multidimensional and dynamic process that requires and interlinks cognitive, metacognitive, and affective dimensions of learning. However, current approaches practiced in computing education research (CER) are not sufficient to capture information beyond the basic programming process data (i.e., IDE‐log data). Therefore, how cognition and affect intertwine and unfold over time in programming problem‐solving activities are rarely investigated.ObjectivesIn this study, we examined how the theory‐informed measures from multimodal data that we have selected as proxies for cognitive and affective dimensions of learning, are associated with student performance, and in comparison, to prior‐knowledge.MethodsA high‐frequency temporal data was collected with a camera, an electroencephalogram, and an eye‐tracker from 40 computer science students (bachelor and master studies) in the context of a code‐debugging activity. To study the cognitive processes associated with learning we focused on cognitive load theory (CLT) and the human information processing model. In addition, we complemented CLT with the model of affective dynamics in learning to avoid the machine reductionism perspective.ResultsOur findings demonstrated that attention, convergent thinking, and frustration were positively correlated with students' successful code‐debugging (i.e., performance), and frequently manifested by high performing participants. Cognitive load, memory load, and boredom were negatively correlated with students' performance, and typically manifested by low performing participants.ImplicationsExtending the context of analysis in reference to student cognitive processes and affective states, affords educators not just to identify lower performers, but also to understand the potential reasons behind their performance, making our method an important contribution in the confluence of CER and the learning technology communities. In addition, the insights extracted from our analyses allow us to discuss potential avenues for improving learning design and the design of interactive learning systems to support the development of students' problem‐solving skills.

Career choice motivation using behavioral strategies

Qualified teachers are a fundamental input for any education system. This paper presents the results of a large-scale intervention to attract high performing high-school students into the teaching profession in Chile. The intervention was a three-arm email campaign which made salient three types of motivations: intrinsic/altruistic, extrinsic, and prestige-related. The “intrinsic” and “prestige” arms reduced applications to teaching majors among high performers, while the “extrinsic” arm increased applications among low performers. A plausible interpretation could be that the “intrinsic” and “prestige” messages made more salient an issue that could otherwise be overlooked by high performing students (typically from more advantaged households), negatively impacting their program choice: that the social value of the teaching still lags behind other professions that are valued more by their families and social circles. In turn, the “extrinsic” arm made salient the recent improvements in the economic conditions of the teaching profession in Chile, thus appealing to low performing students who in general come from disadvantaged families and for whom monetary incentives are potentially more relevant. These results show that making salient certain types of motivations to the wrong target group could lead to undesired results.