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PURPOSE: This study uses deep neural network-generated rim-to-disc area ratio (RADAR) measurements and the disc damage likelihood scale (DDLS) to measure the rate of optic disc rim loss in a large cohort of glaucoma patients. METHODS: A deep neural network was used to calculate RADAR and DDLS for each optic disc photograph (ODP). Patient demographics, diagnosis, intraocular pressure (IOP), and mean deviation (MD) from perimetry were analyzed as risk factors for faster progression of RADAR. Receiver operating characteristic (ROC) curves were used to compare RADAR and DDLS in their utility to distinguish glaucoma from glaucoma suspect (GS) and for detecting glaucoma progression. RESULTS: A total of 13,679 ODPs with evidence of glaucomatous optic nerve damage from 4106 eyes of 2407 patients with glaucoma or GS were included. Of these eyes, 3264 (79.5%) had a diagnosis of glaucoma, and 842 (20.5%) eyes were GS. Mean ± SD baseline RADAR of GS and glaucoma were 0.67 ± 0.13 and 0.57 ± 0.18, respectively (P < 0.001). Older age, greater IOP fluctuation, baseline MD, right eye, and diagnosis of secondary open-angle glaucoma were associated with slope of RADAR. The mean baseline DDLS of GS and glaucoma were 3.78 and 4.39, respectively. Both RADAR and DDLS showed a less steep slope in advanced glaucoma. In glaucoma, the change of RADAR and DDLS correlated with the corresponding change in MD. RADAR and DDLS had a similar ability to discriminate glaucoma from GS and detect disease progression. Area under the ROC curve of RADAR and DDLS was 0.658 and 0.648. CONCLUSIONS: Automated calculation of RADAR and DDLS with a neural network can be used to evaluate the extent and long-term rate of optic disc rim loss and is further evidence of long-term nerve fiber loss in treated patients with glaucoma. TRANSLATIONAL RELEVANCE: Our study provides a large clinic-based experience for RADAR and DDLS measurements in GS and glaucoma with a neural network.

AIM: We tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time points during follow-up. METHODS: 3919 eyes (2259 patients) with ≥2 ODPs at least 2 years apart, and ≥5 24-2 VF exams spanning ≥3 years of follow-up were included. Serial VF mean deviation (MD) rates of change were estimated starting at the fifth visit and subsequently by adding visits until final visit. VF progression was defined as a statistically significant negative slope at two consecutive visits and final visit. We built a twin-neural network with ResNet50-backbone. A pair of ODPs acquired up to a year before the VF progression date or the last VF in non-progressing eyes were included as input. Primary outcome measures were area under the receiver operating characteristic curve (AUC) and model accuracy. RESULTS: The average (SD) follow-up time and baseline VF MD were 8.1 (4.8) years and -3.3 (4.9) dB, respectively. VF progression was identified in 761 eyes (19%). The median (IQR) time to progression in progressing eyes was 7.3 (4.5-11.1) years. The AUC and accuracy for predicting VF progression were 0.862 (0.812-0.913) and 80.0% (73.9%-84.6%). When only fast-progressing eyes were considered (MD rate < -1.0 dB/year), AUC increased to 0.926 (0.857-0.994). CONCLUSIONS: A deep learning model can predict subsequent glaucoma progression from longitudinal ODPs with clinically relevant accuracy. This model may be implemented, after validation, for predicting glaucoma progression in the clinical setting.

PURPOSE: To evaluate and compare the effectiveness of nearest neighbor (NN)- and variational autoencoder (VAE)-smoothing algorithms to reduce variability and enhance the performance of glaucoma visual field (VF) progression models. DESIGN: Longitudinal cohort study. SUBJECTS: 7150 eyes (4232 patients), with ≥ 5 years of follow-up and ≥ 6 visits. METHODS: Vsual field thresholds were smoothed with the NN and VAE algorithms. The mean total deviation (mTD) and VF index rates, pointwise linear regression (PLR), permutation of PLR (PoPLR), and the glaucoma rate index were applied to the unsmoothed and smoothed data. MAIN OUTCOME MEASURES: The proportion of progressing eyes and the conversion to progression were compared between the smoothed and unsmoothed data. A simulation series of noiseless VFs with various patterns of glaucoma damage was used to evaluate the specificity of the smoothing models. RESULTS: The mean values of age and follow-up time were 62.8 (standard deviation: 12.6) years and 10.4 (standard deviation: 4.7) years, respectively. The proportion of progression was significantly higher for the NN and VAE smoothed data compared with the unsmoothed data. VF progression occurred significantly earlier with both smoothed data compared with unsmoothed data based on mTD rates, PLR, and PoPLR methods. The ability to detect the progressing eyes was similar for the unsmoothed and smoothed data in the simulation data. CONCLUSIONS: Smoothing VF data with NN and VAE algorithms improves the signal-to-noise ratio for detection of change, results in earlier detection of VF progression, and could help monitor glaucoma progression more effectively in the clinical setting. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PurposeTo report an image analysis pipeline, DDLSNet, consisting of a rim segmentation (RimNet) branch and a disc size classification (DiscNet) branch to automate estimation of the disc damage likelihood scale (DDLS).DesignRetrospective observational.ParticipantsRimNet and DiscNet were developed with 1208 and 11 536 optic disc photographs (ODPs), respectively. DDLSNet performance was evaluated on 120 ODPs from the RimNet test set, for which the DDLS scores were graded by clinicians. Reproducibility was evaluated on a group of 781 eyes, each with 2 ODPs taken within 4 years apart.MethodsDisc damage likelihood scale calculation requires estimation of optic disc size, provided by DiscNet (VGG19 network), and the minimum rim-to-disc ratio (mRDR) or absent rim width (ARW), provided by RimNet (InceptionV3/LinkNet segmentation model). To build RimNet's dataset, glaucoma specialists marked optic disc rim and cup boundaries on ODPs. The "ground truth" mRDR or ARW was calculated. For DiscNet's dataset, corresponding OCT images provided "ground truth" disc size. Optic disc photographs were split into 80/10/10 for training, validation, and testing, respectively, for RimNet and DiscNet. DDLSNet estimation was tested against manual grading of DDLS by clinicians with the average score used as "ground truth." Reproducibility of DDLSNet grading was evaluated by repeating DDLS estimation on a dataset of nonprogressing paired ODPs taken at separate times.Main outcome measuresThe main outcome measure was a weighted kappa score between clinicians and the DDLSNet pipeline with agreement defined as ± 1 DDLS score difference.ResultsRimNet achieved an mRDR mean absolute error (MAE) of 0.04 (± 0.03) and an ARW MAE of 48.9 (± 35.9) degrees when compared to clinician segmentations. DiscNet achieved 73% (95% confidence interval [CI]: 70%, 75%) classification accuracy. DDLSNet achieved an average weighted kappa agreement of 0.54 (95% CI: 0.40, 0.68) compared to clinicians. Average interclinician agreement was 0.52 (95% CI: 0.49, 0.56). Reproducibility testing demonstrated that 96% of ODP pairs had a difference of ≤ 1 DDLS score.ConclusionsDDLSNet achieved moderate agreement with clinicians for DDLS grading. This novel approach illustrates the feasibility of automated ODP grading for assessing glaucoma severity. Further improvements may be achieved by increasing the number of incomplete rims sample size, expanding the hyperparameter search, and increasing the agreement of clinicians grading ODPs.

BACKGROUND/AIMS: To identify clinical characteristics and factors associated with microcystic macular edema (MME) in patients with primary open-angle glaucoma (POAG). METHODS: We included 315 POAG eyes between 2010 and 2019 with good-quality macular volume scans that had reliable visual fields (VF) available within 6 months in this observational retrospective cohort study. Eyes with retinal pathologies except for epiretinal membrane (ERM) were excluded. The inner nuclear layer was qualitatively assessed for the presence of MME. Global mean deviation (MD) and Visual Field Index (VFI) decay rates, superior and inferior MD rates and pointwise total deviation rates of change were estimated with linear regression. Logistic regression was performed to identify baseline factors associated with the presence of MME and to determine whether MME is associated with progressive VF loss. RESULTS: 25 out of 315 eyes (7.9%) demonstrated MME. The average (±SD) age and MD in eyes with and without MME was 57.2 (±8.7) versus 62.0 (±9.9) years (p=0.02) and -9.8 (±5.7) versus -4.9 (±5.3) dB (p0.49). CONCLUSIONS: More severe glaucoma and younger age were associated with MME. MME was not associated with faster global VF decay in this cohort. MME may confound monitoring of glaucoma with full macular thickness.

PurposeAccurate neural rim measurement based on optic disc imaging is important to glaucoma severity grading and often performed by trained glaucoma specialists. We aim to improve upon existing automated tools by building a fully automated system (RimNet) for direct rim identification in glaucomatous eyes and measurement of the minimum rim-to-disc ratio (mRDR) in intact rims, the angle of absent rim width (ARW) in incomplete rims, and the rim-to-disc-area ratio (RDAR) with the goal of optic disc damage grading.DesignRetrospective cross sectional study.ParticipantsOne thousand and twenty-eight optic disc photographs with evidence of glaucomatous optic nerve damage from 1021 eyes of 903 patients with any form of primary glaucoma were included. The mean age was 63.7 (± 14.9) yrs. The average mean deviation of visual fields was -8.03 (± 8.59).MethodsThe images were required to be of adequate quality, have signs of glaucomatous damage, and be free of significant concurrent pathology as independently determined by glaucoma specialists. Rim and optic cup masks for each image were manually delineated by glaucoma specialists. The database was randomly split into 80/10/10 for training, validation, and testing, respectively. RimNet consists of a deep learning rim and cup segmentation model, a computer vision mRDR measurement tool for intact rims, and an ARW measurement tool for incomplete rims. The mRDR is calculated at the thinnest rim section while ARW is calculated in regions of total rim loss. The RDAR was also calculated. Evaluation on the Drishti-GS dataset provided external validation (Sivaswamy 2015).Main outcome measuresMedian Absolute Error (MAE) between glaucoma specialists and RimNet for mRDR and ARW.ResultsOn the test set, RimNet achieved a mRDR MAE of 0.03 (0.05), ARW MAE of 31 (89)°, and an RDAR MAE of 0.09 (0.10). On the Drishti-GS dataset, an mRDR MAE of 0.03 (0.04) and an mRDAR MAE of 0.09 (0.10) was observed.ConclusionsRimNet demonstrated acceptably accurate rim segmentation and mRDR and ARW measurements. The fully automated algorithm presented here would be a valuable component in an automated mRDR-based glaucoma grading system. Further improvements could be made by improving identification and segmentation performance on incomplete rims and expanding the number and variety of glaucomatous training images.

This study describes the development of a convolutional neural network (CNN) for automated assessment of optic disc photograph quality. Using a code-free deep learning platform, a total of 2377 optic disc photographs were used to develop a deep CNN capable of determining optic disc photograph quality. Of these, 1002 were good-quality images, 609 were acceptable-quality, and 766 were poor-quality images. The dataset was split 80/10/10 into training, validation, and test sets and balanced for quality. A ternary classification model (good, acceptable, and poor quality) and a binary model (usable, unusable) were developed. In the ternary classification system, the model had an overall accuracy of 91% and an AUC of 0.98. The model had higher predictive accuracy for images of good (93%) and poor quality (96%) than for images of acceptable quality (91%). The binary model performed with an overall accuracy of 98% and an AUC of 0.99. When validated on 292 images not included in the original training/validation/test dataset, the model's accuracy was 85% on the three-class classification task and 97% on the binary classification task. The proposed system for automated image-quality assessment for optic disc photographs achieves high accuracy in both ternary and binary classification systems, and highlights the success achievable with a code-free platform. There is wide clinical and research potential for such a model, with potential applications ranging from integration into fundus camera software to provide immediate feedback to ophthalmic photographers, to prescreening large databases before their use in research.

PurposeTo develop a structural metascore (SMS) that combines measurements from different devices and expresses them on a single scale to facilitate their long-term analysis.MethodsThree structural measurements (Heidelberg Retina Tomograph II [HRT] rim area, HD-Cirrus optical coherence tomography [OCT] average retinal nerve fiber layer [RNFL] thickness, Spectralis OCT RNFL global thickness) were normalized on a scale of 0 to 100 and converted to a reference value. The resultant metascores were plotted against time. SMS performance was evaluated to predict future values (internal validation), and correlations between the average grades assigned by three clinicians were compared with the SMS slopes (external validation).ResultsThe linear regression fit with the variance approach, and adjustment to a Spectralis equivalent was the best-performing approach; this was denominated metascore. Plots were created for 3416 eyes of 1824 patients. The average baseline age (± standard deviation) was 69.8 (±13.9), mean follow-up was 11.6 (±4.7) years, and mean number of structural scans per eye was 10.0 (±4.7). The mean numbers of scans per device were 3.8 (±2.5), 5.0 (±2.9), and 1.3 (±3.0) for HRT, Cirrus, and Spectralis, respectively. The metascore slopes' median was -0.3 (interquartile range 1.1). Correlations between the average grades assigned by the three clinicians and the metascore slopes were -0.51, -0.49, and -0.69 for the first (structural measurement printouts alone), second (metascore plots alone), and third (printouts + metascore plots) series of gradings, respectively. The average absolute predictive ability was 7.63/100 (whereas 100 = entire normalized scale).ConclusionsWe report a method that converts Cirrus global RNFL and HRT global rim area normalized measurements to Spectralis global RNFL equivalent values to facilitate long-term structural follow-up.Translational relevanceBecause glaucoma changes usually occur slowly, patients are often examined with different instruments during their follow-up, a method that "unifies" structural measurements provided by different devices, which could assist patients' longitudinal structural follow-up.

This research paper explores how preservice teachers engaged with the transmodal place-based poetry on PhoneMe, an educational social media platform for sharing poetry and vocal performances about place. This work is situated in literature on digital place-based education and theoretical scholarship exploring transmodality and the shifting entanglement of meanings and modes. In this paper, we share findings from a research survey conducted with teacher candidates in a core teacher education literacy course that had transitioned online due to the COVID-19 pandemic. We discuss how participants engaged with the transmodal poetry in the survey and how their perceived poetic meaning and poetic connections changed with each additional modality. We share the pedagogical implications of our findings as well as ideas gleaned from the data for integrating the PhoneMe platform and pedagogy in secondary school classrooms. In the discussion, we explore how engagement with transmodal place-based digital poetry can be a unique way to draw together place-based education, digital literacies, social media literacies, and poetry pedagogy in a way that is visceral, relational, and highly relevant to contemporary lives and classrooms both on and offline.

PurposeWe compared rates of change of macular ganglion cell/inner plexiform (GCIPL) thickness and proportion of worsening and improving rates from 2 optical coherence tomography (OCT) devices in a cohort of eyes with glaucoma.DesignLongitudinal cohort study.MethodsIn a tertiary glaucoma clinic we evaluated 68 glaucoma eyes with ≥2 years of follow-up and ≥4 OCT images. Macular volume scans from 2 OCT devices were exported, coregistered, and segmented. Global and sectoral GCIPL data from the central 4.8 × 4.0-mm region were extracted. GCIPL rates of change were estimated with linear regression. Permutation analyses were used to control specificity with the 2.5 percentile cutoff point used to define "true" worsening. Main outcome measures included differences in global/sectoral GCIPL rates of change between 2 OCT devices and the proportion of negative vs positive rates of change (P < .05).ResultsAverage (standard deviation) 24-2 visual field mean deviation, median (interquartile range) follow-up time, and number of OCT images were -9.4 (6.1) dB, 3.8 (3.3-4.2) years, and 6 (5-8), respectively. GCIPL rates of thinning from Spectralis OCT were faster (more negative) compared with Cirrus OCT; differences were significant in superonasal (P = .03) and superotemporal (P = .04) sectors. A higher proportion of significant negative rates was observed with Spectralis OCT both globally and in inferotemporal/superotemporal sectors (P < .04). Permutation analyses confirmed the higher proportion of global and sectoral negative rates of change with Spectralis OCT (P < .001).ConclusionsChanges in macular GCIPL were detected more frequently on Spectralis' longitudinal volume scans than those of Cirrus OCT. OCT devices are not interchangeable with regard to detection of macular structural progression.

PurposeTo compare local ganglion cell-inner plexiform layer (GCIPL) thickness measurements between 2 OCT devices and to explore factors that may influence the difference in measurements.DesignCross-sectional study.ParticipantsSixty-nine glaucoma eyes (63 patients) with evidence of central damage or mean deviation (MD) of -6.0 dB or worse on a 24-2 visual field (VF).MethodsCirrus and Spectralis OCT macular volume scans were exported, data from the central 20° of both OCT devices were centered and aligned, and 50 × 50 arrays of 0.4° × 0.4° superpixels were created. We estimated nonparametric (Spearman's) correlations and used Bland-Altman plots to compare GCIPL thickness measurements between the two OCTs at the superpixel level. Factors that may have influenced the differences between thickness measurements between the two devices were explored with linear mixed models.Main outcome measuresPooled and individual-eye Spearman's correlation and agreement between thickness measurements from the two devices.ResultsThe median 24-2 VF MD was -6.8 dB (interquartile range [IQR], -4.9 to -12.3 dB). The overall pooled Spearman's correlation between the two devices for all superpixels and eyes was 0.97 (P < 0.001). The median within-eye correlation coefficient was 0.72 (IQR, 0.59-0.79). Bland-Altman plots demonstrated a systematic bias in most individual eyes, with Spectralis GCIPL measurements becoming larger than Cirrus measurements with increasing superpixel thickness. The average superpixel thickness and distance to the fovea influenced the thickness difference between the two devices in multivariate models (P < 0.001).ConclusionsLocal macular thickness measurements from the Spectralis and Cirrus devices are highly correlated, but not interchangeable. Differences in thickness measurements between the two devices are influenced by the location of superpixels and their thickness.

PurposeTo investigate the relationship between longitudinal changes in macular thickness measurements from OCT and changes in central visual field (VF) in patients with glaucoma with central or advanced damage at baseline.DesignLongitudinal cohort study.ParticipantsA total of 116 eyes with ≥3 years of follow-up and ≥5 macular OCT images and central 10° VF tests were selected.MethodsOCT superpixels and VF locations were matched correcting for retinal ganglion cell (RGC) displacement. Superpixel thickness and VF total deviation (TD) values, in both logarithmic and linear scales, were averaged within 3 eccentricities (3.4°, 5.6°, and 6.8°) and superior and inferior hemiretinas and hemifields. We estimated pointwise TD rates of change and rates of change at superpixels for full macular thickness (FMT), ganglion cell complex (GCC), ganglion cell inner plexiform layer (GCIPL), and ganglion cell layer (GCL). Correlation of structure-function (SF) rates of change was investigated with parametric tests. We compared the proportion of worsening and positive slopes for superpixels and VF test locations (negative vs. positive rates of change with P < 0.05) throughout the follow-up period. Permutation analyses were used to control specificity.Main outcome measuresMagnitude of correlation between structural and functional rates of change and proportion of worsening and positive slopes as a function of follow-up time.ResultsThe median (interquartile range) follow-up and number of exams were 4.2 (3.7-4.6) years and 8 (7-9), respectively. The highest correlation of change rates was observed at 3.4° and 5.6° eccentricities (r = 0.24, 0.41, 0.40, and 0.40 for FMT, GCC, GCIPL, and GCL for 3.4° eccentricity and r = 0.28, 0.32, 0.31, and 0.32 for FMT, GCC, GCIPL, and GCL for 5.6° eccentricity, respectively). Although GCC measures demonstrated the highest overall longitudinal SF correlations, the differences were not statistically significant. Significant structural worsening was more frequently detected than functional deterioration at 3- and 5-year time points (P < 0.025). Permutation analyses also confirmed this finding.ConclusionsCorrelations between central structural and functional rates of change were weak to fair in this cohort. Structural changes were detected more frequently than functional changes. Measurements of both structure and function are required for optimal detection of central progression.

PurposeTo quantify visual field (VF) variability as a function of threshold sensitivity and location, and to compare weighted pointwise linear regression (PLR) with unweighted PLR and pointwise exponential regression (PER) for data fit and prediction ability.MethodsTwo datasets were used for this retrospective study. The first was used to characterize and estimate VF variability, and included a total of 4,747 eyes of 3,095 glaucoma patients with six or more VFs and 3 years or more of follow-up. After performing PER for each series, standard deviation of residuals was quantified for each decibel of sensitivity as a measure of variability. A separate dataset was used to test and compare unweighted PLR, weighted PLR, and PER for data fit and prediction, and included 261 eyes of 176 primary open-angle glaucoma patients with 10 or more VFs and 6 years or more of follow-up.ResultsThe degree of variability changed as a function of threshold sensitivity with a zenith and a nadir at 33 and 11 dB, respectively. Variability decreased with eccentricity and was higher in the central 10° (P < 0.001). Differences among the methods for data fit were negligible. PER was the best model to predict future sensitivity values in the mid term and long term.ConclusionsVF variability increases with the severity of glaucoma damage and decreases with eccentricity. Weighted linear regression neither improves model fit nor prediction. PER exhibited the best prediction ability, which is likely related to the nonlinear nature of long-term glaucomatous perimetric decay.Translational relevanceThis study suggests that taking into account heteroscedasticity has no advantage in VF modeling.

Purpose:To compare methods to assess visual field (VF) progression in glaucoma. Methods:4,950 VFs of 253 primary open angle-glaucoma patients were evaluated for progression with the following methods: clinical evaluation, guided progression analysis (GPA), mean deviation (MD), and visual field index (VFI) rates, Advanced Glaucoma Intervention Study (AGIS) and Collaborative Initial Glaucoma Treatment Study (CIGTS) scores, pointwise linear regression (PLR), permutation of PLR (PoPLR), and glaucoma rate index (GRI). A separate simulated series of longitudinal VFs was assessed with all methods except for GPA and clinical evaluation. Results:The average (±SD) age of the patients at baseline was 65.4 (±11.5) years. The average (±SD) follow-up was 11.8 (±4.6) years, and the mean (±SD) number of VFs was 16.8 (±7.0). Proportion of series detected as progressing was 65% for PoPLR, 58% for GRI, 41% for GPA, 40% for PLR, 36% for CIGTS, 35% for clinicians, 31% for MD rate, 29% for AGIS, and 22% for VFI rate. Median times to detection of progression were 7.3 years for PoPLR, 7.5 years for GRI, 11 years for clinicians, 14 years for GPA, 16 years for PLR, 17 years for CIGTS, 19 years for AGIS, and more than 20 years for MD and VFI rates. In simulated VF series, GRI had the highest partial area under the receiver operator characteristic curve (0.040) to distinguish between glaucoma progression and aging/cataract decay, followed by VFI rate (0.028), MD rate (0.024), and PoPLR (0.006). Conclusions:GRI and PoPLR showed the highest proportion of series detected as progressing and shortest times to progression detection. GRI exhibited the best ability to detect progression in the simulated VF series. Translational Relevance:Knowledge of the properties of every method would allow tailoring application in both clinical and research settings.

The longest internal conflict in Colombia was concluded in 2016, leading to the creation of "Cátedra de la Paz," a peace and citizenship education course required for all schools and grade levels. However, teachers' understanding of the course, their ways of implementation and the challenges they face remain unknown. This study examines teachers' understanding of "Cátedra de la Paz" and the challenges they face when implementing it. Data were collected from 45 teachers using qualitative survey, followed by semi-structured interviews with 10 selected participants. Results showed that a) teachers have more integrated views of the course compared to the guideline provided by the Ministry of National Education, and b) teachers face several challenges implementing the course. Findings provide insights about context-oriented learning design for peace education and highlight the need for professional development and relevant support for teachers in a way that considers their understanding and implementation of peace education.

Purpose: Social annotation (SA) is a genre of learning technology that enables the annotation of digital resources for information sharing, social interaction and knowledge production. This study aims to examine the perceived value of SA as contributing to learning in multiple undergraduate courses. Design/methodology/approach: In total, 59 students in 3 upper-level undergraduate courses at a Canadian university participated in SA-enabled learning activities during the winter 2019 semester. A survey was administered to measure how SA contributed to students' perceptions of learning and sense of community. Findings: A majority of students reported that SA supported their learning despite differences in course subject, how SA was incorporated and encouraged and how widely SA was used during course activities. While findings of the perceived value of SA as contributing to the course community were mixed, students reported that peer annotations aided comprehension of course content, confirmation of ideas and engagement with diverse perspectives. Research limitations/implications: Studies about the relationships among SA, learning and student perception should continue to engage learners from multiple courses and from multiple disciplines, with indicators of perception measured using reliable instrumentation. Practical implications: Researchers and faculty should carefully consider how the technical, instructional and social aspects of SA may be used to enable course-specific, personal and peer-supported learning. Originality/value: This study found a greater variance in how undergraduate students perceived SA as contributing to the course community. Most students also perceived their own and peer annotations as productively contributing to learning. This study offers a more complete view of social factors that affect how SA is perceived by undergraduate students.

Purpose: This paper aims to examine the nature and sufficiency of descriptive information included in open datasets and the nature of comments and questions users write in relation to specific datasets. Open datasets are provided to facilitate civic engagement and government transparency. However, making the data available does not guarantee usage. This paper examined the nature of context-related information provided together with the datasets and identified the challenges users encounter while using the resources. Design/methodology/approach: The authors extracted descriptive text provided together with (often at the top of) datasets (N = 216) and the nature of questions and comments users post in relation to the dataset. They then segmented text descriptions and user comments into "idea units" and applied open-coding with constant comparison method. This allowed them to come up with thematic issues that descriptions focus on and the challenges users encounter. Findings: Results of the analysis revealed that context-related descriptions are limited and normative. Users are expected to figure out how to use the data. Analysis of user comments/questions revealed four areas of challenge they encounter: organization and accessibility of the data, clarity and completeness, usefulness and accuracy and language (spelling and grammar). Data providers can do more to address these issues. Research limitations/implications: The purpose of the study is to understand the nature of open data provision and suggest ways of making open data more accessible to "non expert users". As such, it is not focused on generalizing about open data provision in various countries as such provision may be different based on jurisdiction. Practical implications: The study provides insight about ways of organizing open dataset that the resource can be accessible by the general public. It also provides suggestions about how open data providers could consider users' perspectives including providing continuous support. Originality/value: Research on open data often focuses on technological, policy and political perspectives. Arguably, this is the first study on analysis of context-related information in open-datasets. Datasets do not "speak for themselves" because they require context for analysis and interpretation. Understanding the nature of context-related information in open dataset is original idea.

The Patch workshop explores creative/critical analyses that can map the collectively relevant topoi of semiosis in linguistic texts according to the three ecologies as articulated by Félix Guattari. As creative pedagogues both in service and critical of creative economics, we valourize a generative practice, one that results in successive creative readings, writings, visualizations, sonifications and audiovisual artifacts. The Patch is a human-computer procedural algorithm, engaging a series of recursive and recombinant processes that utilize several software programs, collaborative writing and performance practices to bridge analogue and digital literacies. A total of 80 teacher education students, graduate students and faculty, working with a single input text, provided the data reported in this paper.

PurposeTo develop a method to measure the rate of glaucomatous visual field (VF) deterioration and to identify fast progressors.MethodsRetrospective, longitudinal, observational study of 8486 eyes of 4610 glaucomatous patients with ≥6 VFs and ≥3 years of follow-up. A Glaucoma Rate Index (GRI) was calculated. VF locations were partitioned into exponential decay or exponential improvement models. A pointwise rate of change (PRC) was estimated with an exponential fit and expressed as the percent/year change of the age- and location-matched normal perimetric range, presented as a spatially conserved VF map. PRCs were summed and normalized with boundary rates set by simulated decaying and improving VF series on a scale of -100 to +100, respectively.ResultsA total of 89,704 VF examinations with 425,039 test location series was used. Median follow-up and number of VFs/eye were 9.7 years and 9 VFs, respectively. Initial and final mean deviations (±SD) were -4.2 (±5.2) and -5.7 (±6.4) dB. The proportions of test locations designated as decayed, improved, and unchanged were 13%, 4%, and 83%, respectively. Mean PRCs for decay, improvement, and no change were -3.7 (±4.7)%/y, 2.5 (±2.6)%/y, and -0.5 (±2.1)%/y, respectively. The number of eyes with negative and positive GRIs was 5802 (68%) and 2390 eyes (28%), respectively. The proportion of eyes defined as fast progressors was 6.8%.ConclusionsGRI provides a robust measure of glaucomatous VF change, operates without discontinuity over the entire perimetric range, and can be used to identify fast progressors.Translational relevanceThis study describes a novel method that can help the clinician to determine VF progression.

Seismic vulnerability and loss analyses of buildings are usually estimated under the fixed-based condition, omitting the soil-structure interaction (SSI) effects. However, according to the literature and the seismic damage due to past events, mid-to high-rise buildings located on soft soils can present a worse seismic performance. This manuscript aims to investigate whether the SSI effects affect the seismic performance and the losses of reinforced concrete (RC) buildings. To do so, a real 5-storey RC building has been selected as the case study. It was built prior to restrictive Spanish seismic codes. The building was constructed over soft alluvial strata and it has a shallow foundation. The area is characterised by a moderate seismic hazard. Nonlinear static analysis (NLSA) and incremental dynamic analysis (IDA) have been performed to assess the seismic behaviour and the losses expected of the case study building. The calculations have been done in the OpenSees finite-element framework. The direct method has been used to model the SSI. The results have been obtained for the fixed-base and for the SSI models. The numerical outcomes have shown the remarkable effect of the SSI on the fragility and on the performance of these structures. It has been observed that the severe damage expected can be worsened by up to 38% if SSI is taken into account. Additionally, the soft-storey mechanism at the ground floor concentrates all the damage expected, showing that this is the most seismic vulnerable part of the building (owing to higher interstorey-drift ratios and peak floor acceleration values). The losses expected derived for structural and non-structural components have been 140% higher if the SSI is considered.