Content Detection in Handwritten Documents

abstract: Handwritten documents have gained popularity in various domains including education and business. A key task in analyzing a complex document is to distinguish between various content types such as text, math, graphics, tables and so on. For example, one such aspect could be a region on the document with a mathematical expression; in this case, the label would be math. This differentiation facilitates the performance of specific recognition tasks depending on the content type. We hypothesize that the recognition accuracy of the subsequent tasks such as textual, math, and shape recognition will increase, further leading to a better analysis of the document. Content detection on handwritten documents assigns a particular class to a homogeneous portion of the document. To complete this task, a set of handwritten solutions was digitally collected from middle school students located in two different geographical regions in 2017 and 2018. This research discusses the methods to collect, pre-process and detect content type in the collected handwritten documents. A total of 4049 documents were extracted in the form of image, and json format; and were labelled using an object labelling software with tags being text, math, diagram, cross out, table, graph, tick mark, arrow, and doodle. The labelled images were fed to the Tensorflow’s object detection API to learn a neural network model. We show our results from two neural networks models, Faster Region-based Convolutional Neural Network (Faster R-CNN) and Single Shot detection model (SSD).Dissertation/ThesisMasters Thesis Computer Science 201

Innovate Magazine / Annual Review 2009-2010

https://scholarworks.sjsu.edu/innovate/1002/thumbnail.jp

Using Smartpens to Examine and Influence the Relationship between Homework Habits and Academic Achievement in Introductory Engineering Courses

This dissertation examines students’ homework behaviors and their relationship to academic achievement in introductory engineering courses. Much of the prior work examining the relationship between homework and achievement has relied on student self-reports of homework effort. Our results demonstrate that such self-reports are problematic. Instead, we avoid this methodological shortcoming by using smartpens to objectively measure students’ learning activities in an unobtrusive manner and with a high level of fidelity. This dissertation examines how much, how frequently, and when students work on their homework assignments, and if these factors are related to achievement. This dissertation also examines if informing students of their homework behavior influences them to change that behavior and improve achievement. This work makes four major contributions. First, we developed quantitative measures of student homework behavior that are related to academic achievement. Second, we demonstrate that self-reported measures of student homework effort are problematic. Third, we show that measures of homework effort early in a course are nearly as effective at predicting achievement as measures from the entire course. This result suggests that student behavior does not change significantly over a course. Finally, we show that informing students of their homework behaviors, and providing suggestions for improving those behaviors, is an insufficient motivator to change behaviors and improve achievement. This result suggests a two-stage model of metacognition for study behaviors, requiring both monitoring (i.e., being aware of how one is studying) and regulation (i.e., adjusting how one studies based on feedback) to affect changes in behavior.This work makes both applied and methodological contributions to educational research. In contrast to existing research, our results demonstrate a strong and consistent relationship between students’ homework behaviors and academic achievement. Additionally, this work shows that students’ homework behaviors are established early in a course, and tend to remain relatively constant throughout a course.This work highlights the potential of educational data mining and smartpen technology for educational research. Our results confirm the unreliability of studies employing self-reports. Our studies also speak to the value of replication in education research

The University of Montana: Institutional Mythology and Historical Reality

This unedited draft manuscript is Volume 1 of University of Montana President and Professor Emeritus George M. Dennison’s history of The University of Montana. Dennison\u27s tenure as President of the University of Montana was the longest in the institution\u27s history. In office from 1990 until 2010, his connection to UM began well before he served in any executive capacity. As a student he earned both his B.A. and M.A. degrees in history from the university in 1962 and 1963 respectively. After retiring, Dennison returned to his roots as a historian. Focusing on the institution that played such a pivotal role in his life, Dennison began researching and writing a comprehensive history of the University of Montana. He produced a partial manuscript prior to his death in 2017. As indicated by its title, The University of Montana: Institutional Mythology and Historical Reality, Dennison\u27s manuscript seeks to explore how mythology and reality intertwine in the historical narrative of the school. He wrote much of it against the backdrop of the U.S. Presidential election of 2016, and states in his preface that his belief in the imperative for historical truth informed his approach to this research. Dennison breaks the history of the university into seven distinct periods, which span from its founding in 1893 to present day. Delving into each of the university\u27s presidential administrations, Dennison analyzes how major developments in UM history unfolded within the larger context of Montana state politics and, at times, national and international events. Taking a particular lens to the advent of the land-grant college, Dennison contends that dueling beliefs about the fundamental purpose of higher education set the institution, in its infancy, on a long and meandering path to its eventual mature university status. While charting the course, Dennison explores the role of enduring campus myths alongside the actual strategies, accomplishments, and failures of the people who built the University of Montana over the last century. Dennison considered this manuscript Volume 1 of his institutional history. In it he refers to Chapters 4, 5 and 6 and an Epilogue that are not present in this draft.https://scholarworks.umt.edu/theuniversityofmontana/1000/thumbnail.jp

TONGS: TLDR; Opinion Network Guide System

In the modern world, huge amounts of text are being generated every minute. For example, Twitter users post their current emotions in tweets, while Facebook users vent about their experience in posts. In just one minute, Twitter users upload 350,000 tweets, and Facebook users post anywhere from 2.5 million to 3 million posts. To keep up with this growth in data, almost all of this information goes through automated text processing. To extract features such as the opinion and subjectivity in text, sentiment analysis is applied to the corpus. In this thesis, we present the TONGS library for conducting sentiment analysis. TONGS uses Word2Vec within the TensorFlow library to convert words into vector space representations. The TONGS library contains four different methods built upon previous research in sentiment analysis and Word2Vec. We further experiment and analyze these methods using the IMDB dataset. Finally, we introduce and test a new sentiment dataset from government hearings obtained through Digital Democracy, challenging the accuracy of the TONGS library in an unknown topic

The sequence matters: A systematic literature review of using sequence analysis in Learning Analytics

Describing and analysing sequences of learner actions is becoming more popular in learning analytics. Nevertheless, the authors found a variety of definitions of what a learning sequence is, of which data is used for the analysis, and which methods are implemented, as well as of the purpose and educational interventions designed with them. In this literature review, the authors aim to generate an overview of these concepts to develop a decision framework for using sequence analysis in educational research. After analysing 44 articles, the conclusions enable us to highlight different learning tasks and educational settings where sequences are analysed, identify data mapping models for different types of sequence actions, differentiate methods based on purpose and scope, and identify possible educational interventions based on the outcomes of sequence analysis.Comment: Submitted to the Journal of Learning Analytic

Principals and Technology: A Case Study of the Use and Perceived Effectiveness of Technology to Communicate with Constituents

Among the many qualities or attributes that serve as the framework for school leadership development programs, communication is repeatedly noted as being an important facet of the administrative leader’s repertoire (Finch, Gregson & Faulkner, 1992; Gougeon, 1991). It is not enough for a leader to be concerned only about communicating with constituents; it is essential that the leader also considers the effectiveness of this communication (Gougeon, 1991). The consolidation of schools in West Virginia over the past forty years has placed more importance on the ability of a principal to communicate with staff, other administrators, students, parents and communities. Each consolidated school must reach a wider range of staff and a wider demographic range than the smaller one room school of yesterday. The relationship of the public school to the community and the role of the school in sustaining the community have also been a concern when consolidating. The wide ranges of media sources today offers community members the opportunity to share information and opinions through many types of tools, or even create their own media streams to communicate with a targeted audience (Conners, 2000). The 21st century has witnessed the rapid growth of Web 2.0 tools, which are especially helpful in the three areas of transforming communications: advocacy, networking, and collaboration. Networking through technology can form powerful alliances, connecting leaders and experts locally, nationally and internationally (Soulé, 2008). Valentine (1981) asserts that most principals spend approximately seventy-five percent of their days communicating with constituents. Media and technology are converging with new methods of communication. The types of communication that are emerging will rapidly change the way in which we communicate with each other (Killian, 2009). This study provides information related to the methods of communication principals use with their constituencies and their perceived effectiveness of these methods. This information may assist those who prepare develop professional development programs that aid school leaders and develop coursework for 21st century principals in the area of communication