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The utility of teacher evaluation depends on meaningful teacher engagement with evaluation processes. However, critics of traditional tenure policies suggest that tenure nullifies teacher engagement with practices to improve performance. This study investigates whether tenured teachers exhibit perceptions that reflect disengagement with evaluation and feedback and whether perceptions of evaluation vary based on teachers having received tenure under a reformed system that restricts lifetime protections conditional on continual high evaluation scores. Using statewide administrative and survey data from Tennessee, the study employs propensity score methods to isolate differences in perceptions between comparable groups of tenured and untenured teachers. Results show that tenured teachers report more negative perceptions of evaluation in their district; however, this difference is concentrated among those who received permanent tenure status prior to the reformed tenure system. The study concludes by discussing implications and directions for future research.

Most education agencies have implemented new teacher evaluation systems that promise to improve teacher performance. Post-observation performance feedback is a theoretically important driver of this promise as it should ultimately develop teacher-specific weaknesses. This is the first large-scale study to use the written feedback provided to early-career teachers during formal post-observation conferences and quantitatively link critical feedback characteristics (CFCs) to measures of teacher human capital. We find that most conferences do not include CFCs, that feedback is typically unidimensional, and that less effective early-career teachers receive higher shares of CFCs. However, goal-setting is the only CFC associated with subsequent teacher performance. Beginning and less-educated teachers, for whom goal-setting may clarify performance expectations, drive this relationship.

We present a conceptual framework of teacher turnover, which is guided by the broader employee turnover literature and supported by the empirical literature. Synthesising nearly forty years of inte...

This paper examines how teacher opinions towards performance pay policies change when participating in a performance pay program. We use data from an RCT of 220 6th grade math teachers in 193 schoo...

Theory suggests that strategic compensation can not only serve as a powerful motivational incentive to increase worker performance, but also improve the composition of the workforce through the attraction and retention of high performers and discouragement of lesser performers from entering or staying in the profession. This study tests the compositional hypothesis of employment relationships by studying teacher compensation and incentives. In Tennessee, some school districts developed incentive pay plans that provided teachers with bonus awards, while other districts incorporated incentive pay into their salary schedules. This article uses panel data on individual teachers and a difference-in-differences instrumental-variables approach to examine the impact of those incentive programs on teacher retention. We also draw on detailed strategic compensation plan information, including award payout information, to examine the relationship between type of strategic compensation system and teacher turnover. Our analysis suggests that the strategic compensation programs in Tennessee had a significant impact on teacher turnover in participating schools. Implications for educational research, practice, and policy are discussed.

In e-commerce, user reviews can play a significant role in determining the revenue of an organisation. Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies’ reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions.

Background Our ability to acquire, refine and adapt skilled limb movements is a hallmark of human motor learning that allows us to successfully perform many daily activities. The capacity to acquire, refine and adapt other features of motor performance, such as visual search, eye-hand coordination and visuomotor decisions, may also contribute to motor learning. However, the extent to which refinements of multiple behavioral features and their underlying neural processes independently contribute to motor learning remains unknown. In the current study, we used an ethological approach to test the hypothesis that practice-related refinements of multiple behavioral features would be independently predictive of motor learning. Methods Eighteen healthy, young adults used an upper-limb robot with eye-tracking to practice six trials of a continuous, visuomotor task once a week for six consecutive weeks. Participants used virtual paddles to hit away 200 “Targets” and avoid hitting 100 “Distractors” that continuously moved towards them from the back of the workspace. Motor learning was inferred from trial-by-trial acquisition and week-by-week retention of improvements on two measures of task performance related to motor execution and motor inhibition. Adaptations involving underlying neural processes were inferred from trial-by-trial acquisition and week-by-week retention of refinements on measures of skilled limb movement, visual search, eye-hand coordination and visuomotor decisions. We tested our hypothesis by quantifying the extent to which refinements on measures of multiple behavioral features (predictors) were independently predictive of improvements on our two measures of task performance (outcomes) after removing all shared variance between predictors. Results We found that refinements on measures of skilled limb movement, visual search and eye-hand coordination were independently predictive of improvements on our measure of task performance related to motor execution. In contrast, only refinements of eye-hand coordination were independently predictive of improvements on our measure of task performance related to motor inhibition. Conclusion Our results provide indirect evidence that refinements involving multiple, neural processes may independently contribute to motor learning, and distinct neural processes may underlie improvements in task performance related to motor execution and motor inhibition. This also suggests that refinements involving multiple, neural processes may contribute to motor recovery after stroke, and rehabilitation interventions should be designed to produce refinements of all behavioral features that may contribute to motor recovery.

In global Supply Chain Quality Management (SCQM), there are methods to ensure continuity of supply, at appropriate prices while meeting quality standards. But they are limited by the large and fallible role of humans, especially in manufacturing and quality control. Open Manufacturing aims to establish an open network of manufacturers that enables product information sharing among others in the production chain. Realizing the goals of open manufacturing requires processing the large datasets inherent to the production chain partners. But it is difficult to comply with the trust and privacy requirements of the suppliers in a supply chain. This paper presents a comprehensive survey of blockchain and its current and potential use in the industrial context including Industrial Internet of Things (IIoT)-based sensor networks, and the impact of the blockchain technology in manufacturing related areas such as traceability. The focus of this paper is on a potential application of blockchain in SCQM with detailed analysis of its technical feasibility and the methods of transitioning between the current approaches to a blockchain-oriented platform. An innovative use of blockchain to store referral information is also discussed. The survey concludes that blockchain along with IIoT can improve SCQM in an Open Manufacturing model.

The federal Race to the Top initiative signified a shift in American education policy whereby accountability efforts moved from the school to the teacher level. Using administrative data from Tennessee, we explore whether evaluation reforms differentially influenced mobility patterns for teachers of varying effectiveness. We find that the rollout of a statewide evaluation system, even without punitive consequences, was associated with increased turnover; however, there was comparably greater retention of more effective teachers, with larger differences in turnover between highly and minimally effective teachers confined to urban districts and low-performing schools. These results imply that states and districts can increase exit rates of low-performing instructors in the absence of automatic dismissals, which is a pattern that our analyses suggest may not generalize beyond urban school settings.

There is continued concern over the inequitable distribution of highly experienced and effective teachers among historically marginalized student populations. Using longitudinal data from Tennessee, this study assesses whether students of racially/ethnically minoritized and economically disadvantaged backgrounds have unequal exposure to teachers based on alternate definitions of teaching quality. We find minoritized and economically disadvantaged students are 5 to 15 percentage points less likely to be exposed to high-quality teachers. These teacher quality gaps tend to be the largest in urban school contexts. Moreover, school-level exposure gaps tend to be associated with several school, district, and neighborhood factors.

Research has established that racially isolated schools with high concentrations of low-income students disproportionately struggle to recruit and retain highly effective teachers, limiting disadvantaged students’ exposure to high-quality instruction and driving institutional and community instability. This study estimates the effect of selective retention bonuses (SRB) for highly effective teachers on low-performing, high poverty schools’ ability to elevate student performance by increasing access to effective instruction. The theory of action behind the bonus program is simple: SRBs result in greater numbers of highly effective teachers at participating schools, who subsequently drive larger student gains than the teachers who would otherwise fill their positions. To examine whether students in high poverty schools benefit from retention of highly effective teachers, we use differences in eligibility for schools to offer bonuses and the discrete timing of the program in a matched sample, difference-in-differences framework. Results indicate that schools who offered SRBs saw greater test score gains in subsequent years, especially on state reading exams.

Taking time to identify expected products and waiting for the checkout in a retail store are common scenes we all encounter in our daily lives. The realization of automatic product recognition has great significance for both economic and social progress because it is more reliable than manual operation and time-saving. Product recognition via images is a challenging task in the field of computer vision. It receives increasing consideration due to the great application prospect, such as automatic checkout, stock tracking, planogram compliance, and visually impaired assistance. In recent years, deep learning enjoys a flourishing evolution with tremendous achievements in image classification and object detection. This article aims to present a comprehensive literature review of recent research on deep learning-based retail product recognition. More specifically, this paper reviews the key challenges of deep learning for retail product recognition and discusses potential techniques that can be helpful for the research of the topic. Next, we provide the details of public datasets which could be used for deep learning. Finally, we conclude the current progress and point new perspectives to the research of related fields.

Plexiform neurofibroma, a benign peripheral nerve tumor, is associated with the biallelic loss of function of the NF1 tumor suppressor in Schwann cells. Here, we show that FLLL32, a small molecule inhibitor of JAK2/STAT3 signaling, reduces neurofibroma growth in mice with conditional, biallelic deletion of Nf1 in the Schwann cell lineage. FLLL32 treatment or Stat3 deletion in tumor cells reduced inflammatory cytokine expression and tumor macrophage numbers in neurofibroma. Although STAT3 inhibition downregulated the chemokines CCL2 and CCL12, which can signal through CCR2 to recruit macrophages to peripheral nerves, deletion of Ccr2 did not improve survival or reduce macrophage numbers in neurofibroma-bearing mice. Interestingly, Iba1+; F4/80+;CD11b+ macrophages accounted for ~20-40% of proliferating cells in untreated tumors. FLLL32 suppressed macrophage proliferation, implicating STAT3-dependent, local proliferation in neurofibroma macrophage accumulation, and decreased Schwann cell proliferation and increased Schwann cell death. The functions of STAT3 signaling in neurofibroma Schwann cells and macrophages, and its relevance as a therapeutic target in neurofibroma, merit further investigation.

Like other public workers, teachers typically receive high and compressed ratings that do little to differentiate them based on performance. Motivated by empirical evidence of substantial variation...

Spurred by the federal Race to the Top competition, the state of Tennessee implemented a comprehensive statewide educator evaluation system in 2011. The new system is designed to increase the rigor of evaluations and better differentiate teachers based on performance. The use of more differentiated ratings represents a significant shift in education policy. We merge teacher performance evaluations from the new system with data from post-evaluation teacher surveys to examine the effects of the differentiated ratings on job satisfaction for teachers. Using a regression-discontinuity design, we show that higher ratings under the new system causally improve teachers’ perceptions of work relative to lower ratings. Our findings offer the first causal evidence of which we are aware on the relationship between performance ratings and job satisfaction for individual teachers.

The No Child Left Behind Act (NCLB) has been criticized for encouraging schools to neglect students whose performance exceeds the proficiency threshold or lies so far below it that there is no reasonable prospect of closing the gap during the current year. We examine this hypothesis using longitudinal data from 2002–03 through 2005–06. Our identification strategy relies on the fact that as NCLB was phased in, states had some latitude in designating which grades were to count for purposes of a school making adequate yearly progress. We compare the mathematics achievement distribution in a grade before and after it became a high-stakes grade. We find in general no evidence that gains were concentrated on students near the proficiency standard at the expense of students scoring much lower, though there are inconsistent signs of a trade-off with students at the upper end of the distribution.

We report findings from a quasi-experimental evaluation of the recently implemented US$5,000 retention bonus program for effective teachers in Tennessee’s Priority Schools. We estimate the impact of the program on teacher retention using a fuzzy regression discontinuity design by exploiting a discontinuity in the probability of treatment conditional on the composite teacher effectiveness rating that assigns bonus eligibility. Point estimates for the main effect of the bonus are not different from zero. However, for teachers of tested subjects and grades, the program has a consistently positive effect that is both statistically and substantively significant. We hypothesize that the null finding for the main effect is driven by teachers of untested subjects and grades given the amount of weight Tennessee’s teacher evaluation system attributes to school-level performance. This creates a strong incentive to exit the Priority Schools that are by definition low performing. Implementation concerns, including the timing of application process and observed noncompliance in bonus distribution, present obstacles for both the program’s effectiveness and its evaluation.

Pay-for-performance is a popular public education reform, but there is little evidence about the characteristics of a well-designed incentive pay plan for teachers. Some of the literature suggests that effective incentive plans must offer relatively large awards to induce behavioral changes. On the other hand, the experimental economics literature suggests that plans with only a handful of awardees can be less effective at changing behavior than plans that offer an array of possible awards. Still other research suggests that group-based incentives are the most effective strategy when teamwork and cooperation are integral to the production process—as is arguably the case in education. This study takes advantage of a pilot pay-for-performance program in Texas to explore incentive design not only from the perspective of the employer—by examining changes in teacher productivity and retention—but also from the perspective of the employee—by examining the preferences revealed by the incentives teachers design for themselves. We find that when given the opportunity, teachers design relatively weak, group-oriented incentive pay plans. In turn, those relatively weak incentives do not appear to be associated with any significant changes in teacher productivity, although they are correlated with teacher turnover, which, in the long run, could theoretically improve student outcomes.