Hybrid maize seed production in Africa is dependent upon manual detasseling of the female parental lines, often resulting in plant damage that can lead to reduced seed yields on those detasseled lines. Additionally, incomplete detasseling can result in hybrid purity issues that can lead to production fields being rejected. A unique nuclear genetic male sterility seed production technology, referred to as Ms44-SPT, was developed to avoid hybrid seed loss and to improve the purity and quality of hybrid maize production. Hybrid seed yield reduction following detasseling can be attributed to leaf loss. Our analyses showed an average 2.9 leaves are lost during the detasseling process, resulting in a seed yield reduction of 14.0%. These findings suggest that deploying the Ms44-SPT technology would avoid this seed yield loss. By simplifying hybrid production and increasing seed yields, Ms44-SPT could help drive hybrid replacement, providing smallholder farmers with better access to improved hybrids.

Objective Glucose self-monitoring is critical for the management of diabetes in pregnancy, and increased adherence to testing is associated with improved obstetrical outcomes. Incentives have been shown to improve adherence to diabetes self-management. We hypothesized that use of financial incentives in pregnancies complicated by diabetes would improve adherence to glucose self-monitoring. Study Design We conducted a single center, randomized clinical trial from May 2016 to July 2019. In total, 130 pregnant patients, <29 weeks with insulin requiring diabetes, were recruited. Participants were randomized in a 1:1:1 ratio to one of three payment groups: control, positive incentive, and loss aversion. The control group received $25 upon enrollment. The positive incentive group received 10 cents/test, and the loss aversion group received $100 for >95% adherence and “lost” payment for decreasing adherence. The primary outcome was percent adherence to recommended glucose self-monitoring where adherence was reliably quantified using a cellular-enabled glucometer. Adherence, calculated as the number of tests per day divided by the number of recommended tests per day×100%, was averaged from time of enrollment until admission for delivery. Results We enrolled 130 participants and the 117 participants included in the final analysis had similar baseline characteristics across the three groups. Average adherence rates in the loss aversion, control and positive incentive groups were 69% (SE=5.12), 57% (SE = 4.60), and 58% (SE=3.75), respectively (p=0.099). The loss aversion group received an average of $50 compared with $38 (positive incentive) and $25 (control). Conclusion In this randomized clinical trial, loss aversion incentives tended toward higher adherence to glucose self-monitoring among patients whose pregnancies were complicated by diabetes, though did not reach statistical significance. Further studies are needed to determine whether use of incentives improve maternal and neonatal outcomes. Key Points

ABSTRACT Commercialization and utilization of pearl millet ( Pennisetum glaucum L.) by consumers and processing industry is constrained due to rapid onset of rancidity in its milled flour. We studied the underlying biochemical and molecular mechanisms to flour rancidity in contrasting inbreds under 21-day accelerated storage. Rapid TAG decrease was accompanied by FFA increase in high rancidity genotype compared to the low rancidity line, that maintained lower FFA and high TAG levels, besides lower headspace aldehydes. DNA sequence polymorphisms observed in two lipase genes revealed loss-of-function mutations that were functionally confirmed in yeast system. We outline a direct mechanism for mutations in these key TAG lipases in pearl millet and the protection of TAG and fatty acids from hydrolytic and oxidative rancidity respectively,. Natural variation in the PgTAGLip1 and PgTAGLip2 genes may be selected through marker assisted breeding or by precision genetics methods to develop hybrids with improved flour shelf life.

Adaptive learning and assessment systems support learners in acquiring knowledge and skills in a particular domain. The learners’ progress is monitored through them solving items matching their level and aiming at specific learning goals. Scaffolding and providing learners with hints are powerful tools in helping the learning process. One way of introducing hints is to make hint use the choice of the student. When the learner is certain of their response, they answer without hints, but if the learner is not certain or does not know how to approach the item they can request a hint. We develop measurement models for applications where such on-demand hints are available. Such models take into account that hint use may be informative of ability, but at the same time may be influenced by other individual characteristics. Two modeling strategies are considered: (1) The measurement model is based on a scoring rule for ability which includes both response accuracy and hint use. (2) The choice to use hints and response accuracy conditional on this choice are modeled jointly using Item Response Tree models. The properties of different models and their implications are discussed. An application to data from Duolingo, an adaptive language learning system, is presented. Here, the best model is the scoring-rule-based model with full credit for correct responses without hints, partial credit for correct responses with hints, and no credit for all incorrect responses. The second dimension in the model accounts for the individual differences in the tendency to use hints.

Pearl millet is an important cereal crop of semi-arid regions since it is highly nutritious and climate resilient. However, pearl millet is underutilized commercially due to the rapid onset of hydrolytic rancidity of seed lipids post-milling. We investigated the underlying biochemical and molecular mechanisms of rancidity development in the flour from contrasting inbred lines under accelerated aging conditions. The breakdown of storage lipids (triacylglycerols; TAG) was accompanied by free fatty acid accumulation over the time course for all lines. The high rancidity lines had the highest amount of FFA by day 21, suggesting that TAG lipases may be the cause of rancidity. Additionally, the high rancidity lines manifested substantial amounts of volatile aldehyde compounds, which are characteristic products of lipid oxidation. Lipases with expression in seed post-milling were sequenced from low and high rancidity lines. Polymorphisms were identified in two TAG lipase genes (PgTAGLip1 and PgTAGLip2) from the low rancidity line. Expression in a yeast model system confirmed these mutants were non-functional. We provide a direct mechanism to alleviate rancidity in pearl millet flour by identifying mutations in key TAG lipase genes that are associated with low rancidity. These genetic variations can be exploited through molecular breeding or precision genome technologies to develop elite pearl millet cultivars with improved flour shelf life.

Self-glucose monitoring is critical for management of diabetes in pregnancy, and increased adherence to testing is associated with improved obstetrical outcomes. Incentives have been shown to improve adherence to diabetes self-management. We hypothesized that use of financial incentives in pregnancies complicated by diabetes would improve adherence to self-glucose monitoring. We conducted a single-center randomized controlled trial (RCT) of women with pre-gestational or gestational diabetes requiring insulin. Women < 29 weeks gestation were randomized in a 1:1:1 ratio to one of three payment groups: control, positive incentive and loss aversion. The control group received $25 upon enrollment. The positive incentive group received 10 cents/test, and the loss aversion group received $100 for > 95% adherence and “lost” payment for decreasing adherence. The primary outcome was % adherence to recommended self-glucose monitoring where adherence was reliably quantified using a cellular-enabled glucometer. Adherence, calculated as the # tests per day/# recommended tests per day X 100%, was averaged over the course of study. The study was powered to detect a 10% difference in adherence rates with a 2-sided alpha 0.05 and 80% power. Anticipating a 30% withdrawal rate, we recruited 130 participants. From 3/2016 to 7/2019, we enrolled 130 women and 13 withdrew. Baseline demographics were similar across the three groups. Overall, the loss aversion group displayed higher rates of overall adherence to self-glucose monitoring. Average adherence in the loss aversion, control and positive incentive groups were 69% (+/- 24), 57% (+/- 31) and 58% (+/- 28), respectively (p=0.009). The loss aversion group averaged $50 compared to $38 (positive incentive) and $25 (control). This RCT suggests that loss aversion incentives improve adherence to self-glucose monitoring among women whose pregnancies are complicated by diabetes. Further studies are needed to determine whether use of incentives can improve maternal and neonatal outcomes.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Adaptive learning systems support learners in obtaining knowledge and skills in a particular domain. The progress of the learners is monitored as they continuously solve items which match their level and aim at specific learning goals. Providing learners with hints is a powerful tool in helping the learning process. One way of introducing hints in the system is to make hint use the choice of the learner. When the learner is certain of her response, she may answer without hints, but if she is not certain or simply does not know how to approach the item she may request a hint. When requesting a hint is a learner's choice, it can be treated as a random variable along with response time and accuracy and included in the scoring rule which serves as the basis for developing the measurement model. In this chapter such a model for hint requests, response times and response accuracy is developed. An application to data from an adaptive learning system is presented.

This note aims to elucidate why the Dyad-4PNO model of Kern and Culpepper (2020) can be expected to fit real data reasonably well. The main result is that the Dyad-4PNO approximates a latent tree model. We offer a simple proof of identifiability, and draw some implications for psychological measurement in practice.

Learning and assessment are intrinsically linked. However, the research, tools, and statistical models used within the two fields differ greatly. This has created a disconnect: The goals and missions of educational institutions are codified in the language and ideas of learning, but evaluated, monitored, and administered with the tools of assessment. We propose a novel statistical model, the Master model, capable of being the engine behind a modern learning and assessment system. The Master model combines three key concepts from the assessment and learning literature from the past century: A learning model should be multidimensional and hierarchical and should incorporate learning progressions. The Master model is a multidimensional latent variable model, more specifically a latent class model, that not only ranks learners from best to worst but also provides detailed diagnostic feedback to tell learners what they know, and more importantly, what they don't know. By incorporating a hierarchical structure of the latent variables, the Master model reproduces the positive manifold, a phenomenon that continues to be replicated in assessment data where scores between cognitive tests correlate positively. Finally, expert and data-driven annotation can incorporate learning progressions directly into the latent variables. With these three key concepts, the Master model can track the estimate of a learner’s latent skills, track the efficacy of various educational resources such as videos, and recommend which resources the learner should next focus on in order to maximize their learning.

An extension to a rating system for tracking the evolution of parameters over time using continuous variables is introduced. The proposed rating system assumes a distribution for the continuous responses, which is agnostic to the origin of the continuous scores and thus can be used for applications as varied as continuous scores obtained from language testing to scores derived from accuracy and response time from elementary arithmetic learning systems. Large-scale, high-stakes, online, anywhere anytime learning and testing inherently comes with a number of unique problems that require new psychometric solutions. These include (1) the cold start problem, (2) problem of change, and (3) the problem of personalization and adaptation. We outline how our proposed method addresses each of these problems. Three simulations are carried out to demonstrate the utility of the proposed rating system.

Over the past few decades, cognitive diagnostic models have generated a lot of interest due in large part to the call made by the No Child Left Behind Act of 2001 (No Child Left Behind, Act of 2001 Public Law No. 107–110, § 115. Stat, 1425, 2002) for more formative assessments in learning systems. In this chapter, we provide an overview of learning and assessment systems, including the rise in popularity of online and personalized learning systems; we contrast the role of summative and formative assessments in learning systems; and we provide a review of cognitive diagnostic models and the challenges of retrofitting models to data not designed for cognitive diagnostic models.

Evidence-centered design (ECD) is a framework for the design and development of assessments that ensures consideration and collection of validity evidence from the onset of the test design. Blending learning and assessment requires integrating aspects of learning at the same level of rigor as aspects of testing. In this paper, we describe an expansion to the ECD framework (termed e-ECD) such that it includes the specifications of the relevant aspects of learning at each of the three core models in the ECD, as well as making room for specifying the relationship between learning and assessment within the system. The framework proposed here does not assume a specific learning theory or particular learning goals, rather it allows for their inclusion within an assessment framework, such that they can be articulated by researchers or assessment developers that wish to focus on learning.

Background: Idiopathic intracranial hypertension (IIH) is a condition characterized by increased intracranial pressure of unknown cause. IIH has been shown to be associated with female sex as well as obesity. This genome-wide association study was performed to determine whether genetic variants are associated with this condition. Methods: We analyzed the chromosomal DNA of 95 patients with IIH enrolled in the Idiopathic Intracranial Hypertension Treatment Trial and 95 controls matched on sex, body mass index, and self-reported ethnicity. The samples were genotyped using Illumina Infinium HumanCoreExome v1-0 array and analyzed using a generalized linear mixed model that accounted for population stratification using multidimensional scaling. Results: A total of 301,908 single nucleotide polymorphisms (SNPs) were evaluated. The strongest associations observed were for rs2234671 on chromosome 2 ( P = 4.93 × 10 −07 ), rs79642714 on chromosome 6 ( P = 2.12 × 10 −07 ), and rs200288366 on chromosome 12 ( P = 6.23 × 10 −07 ). In addition, 3 candidate regions marked by multiple associated SNPs were identified on chromosome 5, 13, and 14. Conclusions: This is the first study to investigate the genetics of IIH in a rigorously characterized cohort. The study was limited by its modest size and thus would have only been able to demonstrate highly significant association on a genome-wide scale for relatively common alleles exerting large effects. However, several variants and loci were identified that might be strong candidates for follow-up studies in other well-phenotyped cohorts.

Learning and assessment systems have grown and taken shape to incorporate concepts from both models for assessment and models for learning. In this paper we argue that a third dimension is necessary. Not only is it important to understand what the capabilities of a learner are, and how to grow and expand these capabilities, but we must consider where the learner is headed; we need to consider models for navigation. This holistic perspective of learning and assessment systems is encapsulated in the extended learning and assessment system, a framework for conducting research. Fundamental to this framework is the role of computational psychometrics to facilitate the abstraction from raw data to conceptual models. We provide several examples of research projects and describe how they fit into the described framework.

In learning, errors are both ubiquitous and inevitable. It is widely understood that these errors may provide a clue about a person's misconceptions. In this article we propose and investigate a model that aims to identify misconceptions from observed errors. We apply the method to single digit multiplication; a domain that is very suitable for the method, is well-studied, and allowed us to analyze over 25,000 error responses from 335 actual learners. The model, derived from the Ising model popular in physics, makes use of a bigraph that links possible errors to possible misconceptions. The error responses were taken from Math Garden, a computerized adaptive practice environment for arithmetic that is widely used in The Netherlands. The results show that the model outperforms a random selection from the observed errors' possible causes, and correctly predicts the possible cause of a person's subsequent error up to over 75% of the time. Finally, we discuss the model, the findings, and the implications.

This chapter focuses on the state-of-the-art modeling approaches used in Intelligent Tutoring Systems (ITSs) and the frameworks for researching and operationalizing individual and group models of performance, knowledge, and interaction. We adapt several ITS methodologies to model team performance as well as individuals’ performance of the team members. We briefly describe the point processes proposed by von Davier and Halpin (2013), and we also introduce the Competency Architecture for Learning in teaMs (CALM) framework, an extension of the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare, Brawner, Goldberg, & Holden, 2012) to be used for team settings.

Few models have been more ubiquitous in their respective fields than Bayesian knowledge tracing and item response theory. Both these models were developed to analyze data on learners. However, the study designs that these models are designed for differ; Bayesian knowledge tracing is designed to analyze longitudinal data while item response theory is built for cross-sectional data. This paper illustrates a fundamental connection between these two models. Specifically, the stationary distribution of the latent variable and the observed response variable in Bayesian knowledge Tracing are related to an item response theory model. This connection between these two models highlights a key missing component: the role of education in these models. A research agenda is outlined which answers how to move forward with modeling learner data.