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Early Detection of At-Risk Students Using LMS Data

Description

Calculus as a math course is important subject students need to succeed in, in order to venture into STEM majors. This thesis focuses on the early detection of at-risk students in a calculus course which can provide the proper intervention that might help them succeed in the course. Calculus has…

Calculus as a math course is important subject students need to succeed in, in order to venture into STEM majors. This thesis focuses on the early detection of at-risk students in a calculus course which can provide the proper intervention that might help them succeed in the course. Calculus has high failure rates which corroborates with the data collected from Arizona State University that shows that 40% of the 3266 students whose data were used failed in their calculus course.This thesis proposes to utilize educational big data to detect students at high risk of failure and their eventual early detection and subsequent intervention can be useful. Some existing studies similar to this thesis make use of open-scale data that are lower in data count and perform predictions on low-impact Massive Open Online Courses(MOOC) based courses. In this thesis, an automatic detection method of academically at-risk students by using learning management systems(LMS) activity data along with the student information system(SIS) data from Arizona State University(ASU) for the course calculus for engineers I (MAT 265) is developed. The method will detect students at risk by employing machine learning to identify key features that contribute to the success of a student. This thesis also proposes a new technique to convert this button click data into a button click sequence which can be used as inputs to classifiers. In addition, the advancements in Natural Language Processing field can be used by adopting methods such as part-of-speech (POS) tagging and tools such as Facebook Fasttext word embeddings to convert these button click sequences into numeric vectors before feeding them into the classifiers. The thesis proposes two preprocessing techniques and evaluates them on 3 different machine learning ensembles to determine their performance across the two modalities of the class.

ContributorsDileep, Akshay Kumar (Author) / Bansal, Ajay (Thesis advisor) / Cunningham, James (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)

Created2021

Predicting Student Dropout in Self-Paced MOOC Course

Description

One persisting problem in Massive Open Online Courses (MOOCs) is the issue of student dropout from these courses. The prediction of student dropout from MOOC courses can identify the factors responsible for such an event and it can further initiate intervention before such an event to increase student success in…

One persisting problem in Massive Open Online Courses (MOOCs) is the issue of student dropout from these courses. The prediction of student dropout from MOOC courses can identify the factors responsible for such an event and it can further initiate intervention before such an event to increase student success in MOOC. There are different approaches and various features available for the prediction of student’s dropout in MOOC courses.In this research, the data derived from the self-paced math course ‘College Algebra and Problem Solving’ offered on the MOOC platform Open edX offered by Arizona State University (ASU) from 2016 to 2020 was considered. This research aims to predict the dropout of students from a MOOC course given a set of features engineered from the learning of students in a day. Machine Learning (ML) model used is Random Forest (RF) and this model is evaluated using the validation metrics like accuracy, precision, recall, F1-score, Area Under the Curve (AUC), Receiver Operating Characteristic (ROC) curve. The average rate of student learning progress was found to have more impact than other features. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5% respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model. The features engineered in this research are predictive of student dropout and could be used for similar courses to predict student dropout from the course. This model can also help in making interventions at a critical time to help students succeed in this MOOC course.

ContributorsDominic Ravichandran, Sheran Dass (Author) / Gary, Kevin (Thesis advisor) / Bansal, Ajay (Committee member) / Cunningham, James (Committee member) / Sannier, Adrian (Committee member) / Arizona State University (Publisher)

Created2021

Invertebrate Zoology Course Outline 1939

ContributorsMarine Biological Laboratory (Woods Hole, Mass.) (Publisher) / Arizona Board of Regents (Publisher)

Created1939

Invertebrate Zoology Course Outline 1944

ContributorsMarine Biological Laboratory (Woods Hole, Mass.) (Publisher) / Arizona Board of Regents (Publisher)

Created1944

Invertebrate Zoology Course Outline 1945

ContributorsMarine Biological Laboratory (Woods Hole, Mass.) (Publisher) / Arizona Board of Regents (Publisher)

Created1945

Embryology Course Outline 1948

ContributorsMarine Biological Laboratory (Woods Hole, Mass.) (Publisher) / Arizona Board of Regents (Publisher, Publisher)

Created1948

Invertebrate Zoology Course Outline 1955

ContributorsMarine Biological Laboratory (Woods Hole, Mass.) (Publisher) / Arizona Board of Regents (Publisher)

Created1955

Thesis: Timeline of Changes in Mammography Guidelines in the United States

Description

Breast cancer affects about 12% of women in the US. Arguably, it is one of the most advertised cancers. Mammography became a popular tool of breast cancer screening in the 1970s, and patient-geared guidelines came from the American Cancer Society (ACS) and the US Preventative Task Force (USPSTF). This research…

Breast cancer affects about 12% of women in the US. Arguably, it is one of the most advertised cancers. Mammography became a popular tool of breast cancer screening in the 1970s, and patient-geared guidelines came from the American Cancer Society (ACS) and the US Preventative Task Force (USPSTF). This research focuses on ACS guidelines, as they were the earliest as well as the most changed guidelines. Mammography guidelines changed over time due to multiple factors. This research has tracked possible causes of those changes. Research began with an extensive literature search of clinical trials, the New York Times and the Washington Post archives, systematic reviews, ACS and USPSTF archives.

ContributorsZiganshina, Dina (Author) / Tuoti, Whitney Alexandra (Editor) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)

Created2021-02-16

Thesis: Reproduction in Science Fiction

Description

Science fiction works can reflect the relationship between science and society by telling stories that are set in the future of ethical implications or social consequences of scientific advancements. This thesis investigates how the concept of reproduction is depicted in popular science fiction works.

ContributorsDayoung Kim, Grace (Author) / Tuoti, Whitney Alexandra (Editor) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)

Created2021-02-10

Thesis: Human Preconception Sex Selection: Informing the Public of Sex Selection Methods and Ethical Considerations

Description

By questioning methods of sex selection since their early development, and often discovering that they are unreliable, scientists have increased the creative and technological capacity of the field of reproductive health. The presentation of these methods to the public, via published books on timing methods and company websites for sperm…

By questioning methods of sex selection since their early development, and often discovering that they are unreliable, scientists have increased the creative and technological capacity of the field of reproductive health. The presentation of these methods to the public, via published books on timing methods and company websites for sperm sorting, increased interest in, and influence of, sex selection within the global society. The purpose of explaining the history, interest, development, and impact of various sex selection methods in the mid-twentieth century based on the information that is available on them today is to show couples which methods have failed and provide them with the knowledge necessary to make an informed decision on how they choose to go about utilizing methods of sex selection.

ContributorsBlight, Alysse (Author) / Tuoti, Whitney Alexandra (Editor) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)

Created2021-02-26

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