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Greenland stadials and interstadials (GS/GI) were millennial climate oscillations during the last glacial period that were originally identified in Greenland ice cores but that have been correlated with environmental change around much of the globe, including in monsoon regimes, with enhanced monsoon rainfall coincident with North Atlantic warming. Hydroclimate variability associated with GS/GI have been investigated in detail using terrestrial (primarily oxygen isotopes in stalagmites) and marine records, particularly for the Southeast Asian monsoon. However, a considerably smaller number of terrestrial records preserve these events in the Indian summer monsoon (ISM), the primary water source for ~2 billion people across South Asia. Here we present the first glacial-age speleothem stable isotope time series from Nepal, located in the ISM regime. UK-1 is a 187 mm tall aragonite stalagmite from the Pokhara Valley of central Nepal, ~150 km west of Kathmandu. The chronology of UK-1, which was established by 8 U/Th dates, all of which fall in stratigraphic order (within the errors), reveals continuous growth from 34,350-31,500 yr BP (Marine Isotope Stage 3); age uncertainties average ±84 yr. Stable isotope samples were measured every 1 mm, corresponding to a temporal resolution of 18 yr. Oxygen isotope ratios range from -5.6‰ to -7.6‰, and share the same timing and structure as Greenland (inter)stadials GS/GI 6 and 5.2 in the NGRIP record. We interpret this as reflecting an amount effect response to a strengthened ISM driven by more (less) poleward migration of the intertropical convergence zone during periods of northern hemisphere warming (cooling). This clear millennial signal in UK-1 is a somewhat unexpected result given that amount effects in oxygen isotopes in precipitation are weak (R^2=0.1) in this area today. UK-1 carbon isotope ratios range from -3‰ to -6‰ (excluding a small number of negative spikes) and exhibit variability coarsely similar to the NGRIP record, with lower (higher) values generally corresponding to GI (GS), possibly due to prior calcite precipitation in voids above the cave concomitant with changes in precipitation. Some periods of antiphasing between carbon and oxygen are also apparent and may reflect flushing of soil carbon dioxide during particularly wet phases. 

The process of synthesizing solutions for mathematical problems is cognitively complex. Students formulate and implement strate- gies to solve mathematical problems, develop solutions, and make connections between their learned concepts as they apply their reasoning skills to solve such problems. The gaps in student knowl- edge or shallowly-learned concepts may cause students to guess at answers or otherwise apply the wrong approach, resulting in errors in their solutions. Despite the complexity of the synthesis process in mathematics learning, teachers’ knowledge and ability to anticipate areas of potential difficulty is essential and correlated with student learning outcomes. Preemptively identifying the common miscon- ceptions in students that result in subsequent incorrect attempts can be arduous and unreliable, even for experienced teachers. This pa- per aims to help teachers identify the subsequent incorrect attempts that commonly occur when students are working on math problems such that they can address the underlying gaps in knowledge and common misconceptions through feedback. We report on a longi- tudinal analysis of historical data, from a computer-based learning platform, exploring the incorrect answers in the prior school years (’15-’20) that establish the commonality of wrong answers on two Open Educational Resources (OER) curricula–Illustrative Math (IM) and EngageNY (ENY) for grades 6, 7, and 8. We observe that incor- rect answers are pervasive across 5 academic years despite changes in underlying student and teacher population. Building on our find- ings regarding the Common Wrong Answers (CWAs), we report on goals and task analysis that we leveraged in designing and develop- ing a crowdsourcing platform for teachers to write Common Wrong Answer Feedback (CWAF) aimed are remediating the underlying cause of the CWAs. Finally, we report on an in vivo study by analyz- ing the effectiveness of CWAFs using two approaches; first, we use next-problem-correctness as a dependent measure after receiving CWAF in an intent-to-treat second, using next-attempt correctness as a dependent measure after receiving CWAF in a treated analysis. With the rise in popularity and usage of computer-based learning platforms, this paper explores the potential benefits of scalability in identifying CWAs and the subsequent usage of crowd-sourced CWAFs in enhancing the student learning experience through re- mediation. 

Over the late Holocene, a variety of hydroclimate-sensitive proxies have identified substantial, multidecadal changes in Indian summer monsoon (ISM) precipitation, the most prominent of which is the “4.2 ka event”. This interval, dated to ~4.2-3.9 ka, is associated with severe droughts across South Asia that are linked to societal change. Given the absence of the 4.2 ka event in polar records, the 4.2 ka event is generally associated with low latitude forcings, but no clear consensus on its origins has been reached. We investigated the ISM response to the 4.2 ka event through analysis of aragonite stalagmites from Siddha cave, formed in the lower Paleozoic Dhading dolomite in the Pokhara Valley of central Nepal (28.0˚N, 84.1˚E; ~850 m.a.s.l.). The climate of this region is dominated by small monthly variations in air temperature (21±5˚C) but strong precipitation seasonality associated with the ISM: ~80% of the annual 3900 mm of rainfall occurs between June and September. High uranium and low detrital thorium abundances in these stalagmites yield precise U/Th ages that all fall within stratigraphic order. These dates reveal continuous growth from 4.30-2.26 ka, interrupted only by a hiatus from 3.27-3.10 ka. Overlap with coeval aragonite stalagmites reveals generally consistent trends in carbon and oxygen isotope ratios, suggesting that these stalagmites reflect environmental variability and not secondary (e.g., kinetic) effects. Many stalagmite-based paleomonsoon reconstructions rely on oxygen isotope ratios, which track amount effects in regional rainfall. However, our on-going rainwater collection and analysis program, as well as a previous study conducted in Kathmandu, 120 km the east of Siddha cave, reveals that amount effects in precipitation are weak in this region, particularly during the monsoon season, and thus we rely instead on carbon isotope ratios, which have been demonstrated to track site-specific effective precipitation. Siddha cave stalagmite carbon isotopes, in contrast to other South Asian proxy records, indicate that ISM rainfall increased at Siddha cave from 4.13-3.91 ka. As a further test of this result, we analyzed uranium abundances in the section spanning 4.3-3.4 ka. Uranium serves as an indicator of prior aragonite precipitation and thus of hydroclimate, and like carbon isotopes, suggests increased ISM rainfall coincident with the 4.2 ka event. This precipitation anomaly is nearly identical in timing and structure but anti-phased with stalagmites from Mawmluh cave, northeastern India. We investigated the climatic origins of this precipitation dipole using observational data from the Global Precipitation Climatology Centre (GPCC) and Hadley Center Sea Ice and Sea Surface Temperature (HadISST) products. Preliminary spatial composites suggest that large precipitation differences between Mawmluh and Siddha caves are associated with SST anomalies in the equatorial Pacific. Additionally, superposed Epoch Analysis shows relatively rapid eastern Indian Ocean cooling during the summer monsoon season coeval with large precipitation differences between these sites. Our findings lend support to a tropical Indo-Pacific origin of the 4.2 ka event. 

Teachers often rely on the use of a range of open-ended problems to assess students’ understanding of mathematical concepts. Beyond traditional conceptions of student open- ended work, commonly in the form of textual short-answer or essay responses, the use of figures, tables, number lines, graphs, and pictographs are other examples of open-ended work common in mathematics. While recent developments in areas of natural language processing and machine learning have led to automated methods to score student open-ended work, these methods have largely been limited to textual an- swers. Several computer-based learning systems allow stu- dents to take pictures of hand-written work and include such images within their answers to open-ended questions. With that, however, there are few-to-no existing solutions that support the auto-scoring of student hand-written or drawn answers to questions. In this work, we build upon an ex- isting method for auto-scoring textual student answers and explore the use of OpenAI/CLIP, a deep learning embedding method designed to represent both images and text, as well as Optical Character Recognition (OCR) to improve model performance. We evaluate the performance of our method on a dataset of student open-responses that contains both text- and image-based responses, and find a reduction of model error in the presence of images when controlling for other answer-level features. 

The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in math- ematics, is a well-established and proven approach in learn- ing analytics. In this work, we report on our analysis exam- ining the generalizability of BKT models across academic years attributed to ”detector rot.” We compare the gen- eralizability of Knowledge Training (KT) models by com- paring model performance in predicting student knowledge within the academic year and across academic years. Models were trained on data from two popular open-source curric- ula available through Open Educational Resources. We ob- served that the models generally were highly performant in predicting student learning within an academic year, whereas certain academic years were more generalizable than other academic years. We posit that the Knowledge Tracing mod- els are relatively stable in terms of performance across aca- demic years yet can still be susceptible to systemic changes and underlying learner behavior. As indicated by the evi- dence in this paper, we posit that learning platforms lever- aging KT models need to be mindful of systemic changes or drastic changes in certain user demographics.