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Altered DNA dynamics at lesion sites are implicated in how DNA repair proteins pause and identify damage within genomic DNA. We examined DNA dynamics in the context of damage recognition by Rad4 (yeast ortholog of XPC), which recognizes diverse lesions from environmental mutagens and initiates nucleotide excision repair. Previous studies with a cytosine-analog FRET pair placed on either side of 3 base-pair (bp) mismatched sites – recognized specifically by Rad4 in vitro – unveiled severely deformed DNA even without Rad4 (Chakraborty et al. (2018) Nucleic Acid Res. 46: 1240-1255). Here, using laser T-jump, we revealed the timescales of these spontaneous deformations. 3-bp AT-rich nonspecific sites, whether matched or mismatched, exhibited conformational dynamics primarily within the T-jump observation window (~20 µs – <100 ms), albeit with some amplitude in unresolved (<20 µs) kinetics. The amplitudes of the “missing” fast kinetics increased dramatically for mismatched specific sites, which were further distinguished by additional “missing” amplitude in slow (>100 ms) kinetics at elevated temperatures. We posit that the rapid (µs-ms) fluctuations help stall a diffusing protein at AT-rich/damaged sites and that the >100-ms kinetics reflect a propensity for specific DNA to adopt unwound/bent conformations that may resemble Rad4-bound structures. These studies provide compelling evidence for unusual DNA dynamics and deformability that likely govern how Rad4 senses DNA damage. 

This exploratory study delves into the complex challenge of analyzing and interpreting student responses to mathematical problems, typically conveyed through image formats within online learning platforms. The main goal of this research is to identify and differentiate various student strategies within a dataset comprising image-based mathematical work. A comprehensive approach is implemented, including various image representation, preprocessing, and clustering techniques, each evaluated to fulfill the study’s objectives. The exploration spans several methods for enhanced image representation, extending from conventional pixel-based approaches to the innovative deployment of CLIP embeddings. Given the prevalent noise and variability in our dataset, an ablation study is conducted to meticulously evaluate the impact of various preprocessing steps, assessing their potency in eradicating extraneous backgrounds and noise to more precisely isolate relevant mathematical content. Two clustering approaches—k-means and hierarchical clustering—are employed to categorize images based on student strategies that underlies their responses. Preliminary results underscore the hierarchical clustering method could distinguish between student strategies effectively. Our study lays down a robust framework for characterizing and understanding student strategies in online mathematics problem-solving, paving the way for future research into scalable and precise analytical methodologies while introducing a novel open-source image dataset for the learning analytics research community. 

Feedback is a crucial factor in mathematics learning and in- struction. Whether expressed as indicators of correctness or textual com- ments, feedback can help guide students’ understanding of content. Be- yond this, however, teacher-written messages and comments can provide motivational and affective benefits for students. The question emerges as to what constitutes effective feedback to promote not only student learning but also motivation and engagement. Teachers may have differ- ent perceptions of what constitutes effective feedback utilizing different tones in their writing to communicate their sentiment while assessing student work. This study aims to investigate trends in teacher senti- ment and tone when providing feedback to students in a middle school mathematics class context. Toward this, we examine the applicability of state-of-the-art sentiment analysis methods in a mathematics context and explore the use of punctuation marks in teacher feedback messages as a measure of tone. 

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. 

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 Indian Summer Monsoon [ISM] provides approximately 80% of South Asia’s annual average precipitation. Nepal represents a particularly important sector of the ISM because of its location at the base of the Himalayas, Asia’s water tower, and in the zone of influence of the mid-latitude westerlies. Late Holocene ISM variability has previously been examined using high resolution resolved stable isotope records of stalagmites from northern, northeastern, and central India, but as of yet, no such records have been published from Nepal. We present high resolution stable isotopic time series from two precisely-dated and partially overlapping stalagmites spanning the last 2400 years from Siddha Baba Cave, central Nepal, as well as a year of isotopic data from rainwater collected near the cave. It has been suggested that the amount effect has only a minor effect on the oxygen isotope variability in precipitation in this area. As a result, we couple oxygen and carbon isotopes from these stalagmites to examine both regional and local-scale ISM dynamics. The Siddha Baba record reveals two periods suggestive of changes in the ISM: an apparent increase in rainfall during approximately CE 1350-1550 and a reduction in rainfall characterizing the last two centuries. We investigate these intervals using the Last Millennium Ensemble, a state-of-the-art suite of climate model simulations conducted by the National Center for Atmospheric Research with the Community Earth System Model. A primary focus is on links between Indo-Pacific ocean-atmosphere interactions and subsequent changes in the monsoon circulation over the Indian subcontinent, as well as regional moisture transport into Nepal between these periods. 

The Indian Summer Monsoon [ISM] provides approximately 80% of South Asia’s annual average precipitation. Nepal represents a particularly important sector of the ISM because of its location at the base of the Himalayas, Asia’s water tower, and in the zone of influence of the mid-latitude westerlies. Late Holocene ISM variability has previously been examined using high resolution resolved stable isotope records of stalagmites from northern, northeastern, and central India, but as of yet, no such records have been published from Nepal. We present high resolution stable isotopic time series from two precisely-dated and partially overlapping stalagmites spanning the last 2400 years from Siddha Baba Cave, central Nepal, as well as a year of isotopic data from rainwater collected near the cave. It has been suggested that the amount effect has only a minor effect on the oxygen isotope variability in precipitation in this area. As a result, we couple oxygen and carbon isotopes from these stalagmites to examine both regional and local-scale ISM dynamics. The Siddha Baba record reveals two periods suggestive of changes in the ISM: an apparent increase in rainfall during approximately CE 1350-1550 and a reduction in rainfall characterizing the last two centuries. We investigate these intervals using the Last Millennium Ensemble, a state-of-the-art suite of climate model simulations conducted by the National Center for Atmospheric Research with the Community Earth System Model. A primary focus is on links between Indo-Pacific ocean-atmosphere interactions and subsequent changes in the monsoon circulation over the Indian subcontinent, as well as regional moisture transport into Nepal between these periods. 

Open-ended questions in mathematics are commonly used by teachers to monitor and assess students’ deeper conceptual understanding of content. Student answers to these types of questions often exhibit a combination of language, drawn diagrams and tables, and mathematical formulas and expressions that supply teachers with insight into the processes and strategies adopted by students in formulating their responses. While these student responses help to inform teachers on their students’ progress and understanding, the amount of variation in these responses can make it difficult and time-consuming for teachers to manually read, assess, and provide feedback to student work. For this reason, there has been a growing body of research in developing AI-powered tools to support teachers in this task. This work seeks to build upon this prior research by introducing a model that is designed to help automate the assessment of student responses to open-ended questions in mathematics through sentence-level semantic representations. We find that this model outperforms previously published benchmarks across three different metrics. With this model, we conduct an error analysis to examine characteristics of student responses that may be considered to further improve the method.