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LearnSphere is a web-based data infrastructure designed to transform scientific discovery and innovation in education. It supports learning researchers in addressing a broad range of issues including cognitive, social, and motivational factors in learning, educational content analysis, and educational technology innovation. LearnSphere integrates previously separate educational data and analytic resources developed by participating institutions. The web-based workflow authoring tool, Tigris, allows technical users to contribute sophisticated analytic methods, and learning researchers can adapt and apply those methods using graphical user interfaces, importantly, without additional programming. As part of our use-driven design of LearnSphere, we built a community through workshops and summer schools on educational data mining. Researchers interested in particular student levels or content domains can find student data from elementary through higher-education and across a wide variety of course content such as math, science, computing, and language learning. LearnSphere has facilitated many discoveries about learning, including the importance of active over passive learning activities and the positive association of quality discussion board posts with learning outcomes. LearnSphere also supports research reproducibility, replicability, traceability, and transparency as researchers can share their data and analytic methods along with links to research papers. We demonstrate the capabilities of LearnSphere through a series of case studies that illustrate how analytic components can be combined into research workflow combinations that can be developed and shared. We also show how open web-accessible analytics drive the creation of common formats to streamline repeated analytics and facilitate wider and more flexible dissemination of analytic tool kits.

73Ge(n,γ) cross sections were measured at the neutron time-of-flight facility n_TOF at CERN up to neutron energies of 300 keV, providing for the first time experimental data above 8 keV. Results indicate that the stellar cross section at kT=30 keV is 1.5 to 1.7 times higher than most theoretical predictions. The new cross sections result in a substantial decrease of 73Ge produced in stars, which would explain the low isotopic abundance of 73Ge in the solar system. Keywords: Nucleosynthesis, Neutron capture, s process, Germanium, n_TOF

The neutron capture cross section of 154Gd was measured from 1 eV to 300 keV in the experimental area located 185 m from the CERN n_TOF neutron spallation source, using a metallic sample of gadolinium, enriched to 67% in 154Gd. The capture measurement, performed with four C6D6 scintillation detectors, has been complemented by a transmission measurement performed at the GELINA time-of-flight facility (JRC-Geel), thus minimising the uncertainty related to sample composition. An accurate Maxwellian averaged capture cross section (MACS) was deduced over the temperature range of interest for s process nucleosynthesis modelling. We report a value of 880(50) mb for the MACS at kT=30 keV, significantly lower compared to values available in literature. The new adopted 154Gd(n,γ) cross section reduces the discrepancy between observed and calculated solar s-only isotopic abundances predicted by s-process nucleosynthesis models. Keywords: s process, 154Gd, Neutron time of flight, n_TOF

This work reports on accurate, high-resolution measurements of the 25Mg(n,γ)26Mg and 25Mg(n,tot) cross sections in the neutron energy range from thermal to about 300 keV, leading to a significantly improved 25Mg(n,γ)26Mg parametrization. The relevant resonances for n+25Mg were characterized from a combined R-matrix analysis of the experimental data. This resulted in an unambiguous spin/parity assignment of the corresponding excited states in 26Mg. With this information experimental upper limits of the reaction rates for 22Ne(α,n)25Mg and 22Ne(α,γ)26Mg were established, potentially leading to a significantly higher (α,n)/(α,γ) ratio than previously evaluated. The impact of these results has been studied for stellar models in the mass range 2 to 25 M⊙.

Abstract Introduction and purpose Mycobacterium (M.) chelonae is responsible for a half of relatively rare nontuberculous mycobacteria (NTM) keratitis. We report a case of M. chelonae keratitis in a woman following sclerocorneal suture extraction after cataract surgery. Results A 70-year-old woman presented with a red eye and corneal infiltration of her left eye six weeks following sclerocorneal suture extraction after an elective cataract surgery in another institute. She complained of a sharp, cutting pain and photophobia. Since initial corneal scrapes and conjunctival swabs proved no pathogen using culture and PCR methods, non-specific antibiotics and antifungal agents were administered. As keratitis was complicated by an inflammation in the anterior chamber and vitreous, samples of the vitreous fluid were sent for microbiologic examination. DNA of Epstein-Barr virus (EBV) was repeatedly detected. Since the intrastromal abscess had formed, corneal re-scrapings were performed and M. chelonae was detected using culture, MALDI-TOF MS and PCR methods. Therapy was changed to a combination of oral and topical clarithromycin, intravitreal, topical and intracameral amikacin, and oral and topical moxifloxacin. The successful therapy led to stabilization. The optical penetrating keratoplasty was performed and no signs of the infection recurrence were found. Conclusions The diagnosis of nontuberculous mycobacterial keratitis is difficult and often delayed. An aggressive and prolonged antimicrobial therapy should include systemic and topical antibiotics. Surgical intervention in the form of corneal transplantation may be required in the active and nonresponsive infection. In the presented case this was necessary for visual rehabilitation due to scarring.

When women receive a diagnosis of a gynecologic malignancy, they can have questions about their diagnosis or treatment that can result in voice queries to virtual assistants for more information. Recent advancement in artificial intelligence (AI) has transformed the landscape of medical information accessibility. The Google virtual assistant (VA) outperformed Siri, Alexa and Cortana in voice queries presented prior to the explosive implementation of AI in early 2023. The efforts presented here focus on determining if advances in AI in the last 12 months have improved the accuracy of Google VA responses related to gynecologic oncology. Previous questions were utilized to form a common basis for queries prior to 2023 and responses in 2024. Correct answers were obtained from the UpToDate medical resource. Responses related to gynecologic oncology were obtained using Google VA, as well as the generative AI chatbots Google Bard/Gemini and Microsoft Bing-Copilot. The AI narrative responses varied in length and positioning of answers within the response. Google Bard/Gemini achieved an 87.5% accuracy rate, while Microsoft Bing-Copilot reached 83.3%. In contrast, the Google VA’s accuracy in audible responses improved from 18% prior to 2023 to 63% in 2024. While the accuracy of the Google VA has improved in the last year, it underperformed Google Bard/Gemini and Microsoft Bing-Copilot so there is considerable room for further improved accuracy.

An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning rate and item difficulty, can be estimated from prior data. A critical function of AIS is to have students practice new content once the AIS predicts that they have 'mastered' current content or learned it to some criterion. For making this prediction, individual student parameters (e.g., for learning rate) are frequently unavailable due to having no prior data about a student, and thus population-level parameters or rules-of-thumb are typically applied instead. In this paper, we will argue and demonstrate via simulation and data analysis that even in best-case scenarios, learner models assuming equal learning rates for students will inevitably lead to systematic errors that result in suboptimal pedagogical decisions for "most" learners. This finding leads us to conclude that systematic errors should be expected, and mechanisms to adjust predictions to account for them should be included in AIS. We introduce two solutions that can adjust for student differences "online" in a running system: one that tracks systemic errors of the learner model (not the student) and adjusts accordingly, and a student-level performance adaptive feature. We demonstrate these solutions' efficacy and practicality on six large educational datasets and show that these features improved model accuracy in all tested datasets. [For the corresponding Grantee Submission, see ED629726.]

The Mobile Fact and Concept Textbook System (MoFaCTS) is an individualized online tutoring system designed to increase information comprehension and retention. It is being implemented in community college anatomy and physiology (A&P) courses for further system development. A&P was selected because it is a very challenging and highly in demand course. MoFaCTS was used to create Cloze (fill-in-the-blank) questions from the course text which students use to practice the course material. The system provides correction and feedback to the student, repeating questions to improve recall. The system also produces detailed progress reports for both faculty and students. A survey given to students showed a moderately positive impression of the systems, with A&P II students responding more positively on some survey items.

A good functional status of cryopreserved boar spermatozoa is very important for successful fertilization of porcine oocytes and in vitro embryo production. The purpose of the study was to evaluate the changes in functional status of boar spermatozoa separated from frozen-thawed semen and capacitated in vitro by caffeine. The effect of acrosome reaction development in spermatozoa on the efficiency of oocyte fertilization has been studied in boars A, B and C. Motile spermatozoa were separated by Percoll gradient, untreated (control) or treated with both 1mM and 2mM caffeine, and capacitated or co-cultured with matured oocytes. The motility, viability, chromatin and acrosome integrity, and fertilizing ability of spermatozoa were assessed. The separation significantly increased (P < 0.05) the percentage of viable spermatozoa in all tested boars and percentages of motile and acrosome intact spermatozoa in boars B and C. The capacitation significantly decreased (P < 0.05) the percentages of viable and motile spermatozoa, but after capacitation, the motility and viability were significantly higher (P < 0.05) for the caffeine-treated spermatozoa than for the untreated controls. A fall in the proportion of acrosome-intact spermatozoa was different for each caffeine concentration and each boar, but in all boars, acrosome reaction progress was faster and, similarly, monospermy and the total efficiency of fertilization were significantly higher (P < 0.01) for the spermatozoa treated with 1mM caffeine than for those treated with 2mM caffeine. It can be concluded that there is a potential relationship between the acrosome reaction progress in frozen-thawed boar spermatozoa and the efficiency of fertilization of porcine oocytes. A faster AR induced in spermatozoa by appropriate caffeine treatment resulted in a higher monospermy rate and total efficiency of fertilization. Thus, it is important to test sires before their semen is used for in vitro embryo production. The faster AR induced by 1mM caffeine was more effective in terms of monospermy and total efficiency of fertilization.

Adaptive learning technology solutions often use a learner model to trace learning and make pedagogical decisions. The present research introduces a formalized methodology for specifying learner models, logistic knowledge tracing (LKT), that consolidates many extant learner modeling methods. The strength of LKT is the specification of a symbolic notation system for alternative logistic regression models that is powerful enough to specify many extant models in the literature and many new models. To demonstrate the generality of LKT, we fit 12 models, some variants of well-known models and some newly devised, to six learning technology datasets. The results indicated that no single learner model was best in all cases, further justifying a broad approach that considers multiple learner model features and the learning context. We introduce features to stand in for student-level intercepts and argue that to be maximally applicable, a learner model needs to adapt to student differences. The results of our comparisons show the general importance of modeling recent learning for all datasets, with special importance for terms that model memory in datasets involving fact learning. [This article was published in "IEEE Transactions on Learning Technologies" (EJ1324365).]

Purpose: The relationship between external load and creatine-kinase (CK) response at the team/position or individual level using Global Positioning Systems (GPS) has been studied. This study aimed to compare GPS-derived and Football Movement Profile (FMP) –derived CK-prediction models for national-team soccer players. The second aim was to compare the performance of general and individualized CK prediction models. Methods: Four hundred forty-four national-team soccer players (under 15 [U15] to senior) were monitored during training sessions and matches using GPS. CK was measured every morning from whole blood. The players had 19.3 (18.1) individual GPS-CK pairs, resulting in a total of 8570 data points. Machine learning models were built using (1) GPS-derived or (2) FMP-based parameters or (3) the combination of the 2 to predict the following days' CK value. The performance of general and individual-specific prediction models was compared. The performance of the models was described by R2 and the root-mean-square error (RMSE, in units per liter for CK values). Results: The FMP model (R2 =.60, RMSE = 144.6 U/L) performed similarly to the GPS-based model (R2 =.62, RMSE = 141.2 U/L) and the combination of the 2 (R2 =.62, RMSE = 140.3 U/L). The prediction power of the general model was better on average (R2 =.57 vs R2 =.37) and for 73% of the players than the individualized model. Conclusions: The results suggest that FMP-based CK-prediction models perform similarly to those based on GPS-derived metrics. General machine learning models' prediction power was higher than those of the individual-specific models. These findings can be used to monitor postmatch recovery strategies and to optimize weekly training periodization. [ABSTRACT FROM AUTHOR]

Despite considerable advances in knowledge tracing algorithms, educational technologies that use this technology typically continue to use older algorithms, such as Bayesian Knowledge Tracing. One key reason for this is that contemporary knowledge tracing algorithms primarily infer next-problem correctness in the learning system, but do not attempt to infer the knowledge the student can carry out of the system, information more useful for teachers. The ability of knowledge tracing algorithms to predict problem correctness using data from intelligent tutoring systems has been extensively researched, but data from outcomes other than next-problem correctness have received less attention. In addition, there has been limited use of knowledge tracing algorithms in games, because algorithms that do attempt to infer knowledge from answer correctness are often too simple to capture the more complex evidence of learning within games. In this study, data from a digital learning game, (anonymized), was used to compare ten knowledge tracing algorithms' ability to predict students' knowledge carried outside the learning system--measured here by posttest scores--given their game activity. All Opportunities Averaged (AOA), a method proposed by Authors (2020) was used to convert correctness predictions to knowledge estimates, which were also compared to the built-in estimates from algorithms that produced them. Although statistical testing was not feasible for these data, three algorithms tended to perform better than the others: Dynamic Key-Value Memory Networks, Logistic Knowledge Tracing, and a multivariate version of Elo. Algorithms' built-in estimates of student ability underperformed estimates produced by AOA, suggesting that some algorithms may be better at estimating performance than ability. Theoretical and methodological challenges related to comparing knowledge estimates with hypothesis testing are also discussed.

Understanding the challenges faced by second language (L2) learners in lexical tone perception is crucial for effective language acquisition. This study investigates the impact of exaggerated acoustic properties on facilitating Mandarin tone learning for English speakers. Using synthesized tone stimuli, we systematically manipulated pitch contours through three key modifications: expanding the fundamental frequency (F0), increasing F0 (female voice), and extending the overall duration. Our objectives were to assess the influence of F0 expansion, higher F0, longer duration, and varied syllables on Mandarin tone learning and generalization. Participants engaged in a non-adaptive trial-by-trial tone identification task. Mixed-effects logistic regression modeling was used to analyze accuracy across learning phases, acoustic factors, and tones. Findings reveal improvements in accuracy from training to testing and generalization phases, indicating the effectiveness of perceptual training to tone perception for adult English speakers. Tone 1 emerged as the easiest to perceive, while Tone 3 posed the most challenge, consistent with established hierarchies of tonal acquisition difficulty. Analysis of acoustic factors highlighted tone-specific effects. Expanded F0 was beneficial for the identification of Tone 2 and Tone 3 but posed challenges for Tone 1 and Tone 4. Additionally, longer durations also exhibited varied effects across tones, aiding in the identification of Tone 3 and Tone 4 but hindering Tone 1 identification. The higher F0 was advantageous for Tone 2 but disadvantageous for Tone 3. Furthermore, the syllable ma facilitated the identification of Tone 1 and Tone 2 but not for Tone 3 and Tone 4. These findings enhance our understanding of the role of acoustic properties in L2 tone perception and have implications for the design of effective training programs for second language acquisition.

Background: Patients undergoing hemodialysis are at high risk for healthcare-associated infections; they are at 100 times the risk of Staphylococcus aureus bloodstream infections (BSI) compared with U.S. adults not on hemodialysis. Prior studies found that nasal decolonization with mupirocin prevented S. aureus BSI among hemodialysis patients. We implemented a nasal decolonization intervention in which patients self-administered povidone-iodine (PVI) at each dialysis session. We aimed to assess: 1) hemodialysis patients’ knowledge of their infection risk and their willingness to take an active role in infection prevention; 2) the acceptability of the PVI nasal decolonization intervention. Methods: We performed a stepped wedge cluster randomized trial at 16 outpatient hemodialysis centers. Patients were surveyed: before starting PVI, 1 month after their center started using PVI, and ~6 months after starting PVI. We used a chi-square test to compare results. Results: 469 patients completed at least 1 survey: 400 pre-intervention, 237 at 1 month and 201 at 6 months. Overall, 56% of patients thought that their risk of infection was average or below average compared with an average person in the U.S. (Figure). Over 98% agreed with the statement “One of the most important things I can do for my health is to take an active role in my health care." In the pre-intervention survey, 73% were willing to do “a lot of effort” to prevent an infection. This proportion was similar (73%) in the 2nd survey, but decreased to 63% in the final survey (p < 0 .01). Among 106 patients who reported starting PVI, 85% reported that PVI felt neutral or pleasant, 9.4% reported a side effect, and 79% reported using it during the past 3 dialysis sessions. Among 102 patients who reported using PVI at 6 months, 87% said it felt neutral/pleasant, 3.9% reported a side effect and 75% reported using it during the past 3 dialysis sessions. Side effects included nasal dripping, congestion or burning/stinging, unpleasant smell, headache, yellow tears, and minor nose bleeding. Conclusions: Hemodialysis patients are not aware of their high risk of infection. Although many were willing to expend a lot of effort to prevent an infection, this willingness decreased during an infection prevention intervention. There were few PVI side effects and most patients stated that PVI felt neutral/pleasant, yet many patients chose to not use PVI. Future research should aim to improve patient education on their risk of infection and assess barriers to adherence with infection prevention interventions.