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Graph Search as a Feature in Imperative/Procedural Programming Languages

Description

Graph theory is a critical component of computer science and software engineering, with algorithms concerning graph traversal and comprehension powering much of the largest problems in both industry and research. Engineers and researchers often have an accurate view of their

Graph theory is a critical component of computer science and software engineering, with algorithms concerning graph traversal and comprehension powering much of the largest problems in both industry and research. Engineers and researchers often have an accurate view of their target graph, however they struggle to implement a correct, and efficient, search over that graph.

To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.

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Date Created
2018

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A comparative analysis of graph vs relational database for instructional module development system

Description

In today's data-driven world, every datum is connected to a large amount of data. Relational databases have been proving itself a pioneer in the field of data storage and manipulation since 1970s. But more recently they have been challenged by

In today's data-driven world, every datum is connected to a large amount of data. Relational databases have been proving itself a pioneer in the field of data storage and manipulation since 1970s. But more recently they have been challenged by NoSQL graph databases in handling data models which have an inherent graphical representation. Graph databases with the ability to store physical relationships between two nodes and native graph processing technique have been doing exceptionally well in graph data storage and management for applications like recommendation engines, biological modeling, network modeling, social media applications, etc.

Instructional Module Development System (IMODS) is a web-based software system that guides STEM instructors through the complex task of curriculum design, ensures tight alignment between various components of a course (i.e., learning objectives, content, assessments), and provides relevant information about research-based pedagogical and assessment strategies. The data model of IMODS is highly connected and has an inherent graphical representation between all its entities with numerous relationships between them. This thesis focuses on developing an algorithm to determine completeness of course design developed using IMODS. As part of this research objective, the study also analyzes the data model for best fit database to run these algorithms. As part of this thesis, two separate applications abstracting the data model of IMODS have been developed - one with Neo4j (graph database) and another with PostgreSQL (relational database). The research objectives of the thesis are as follows: (i) evaluate the performance of Neo4j and PostgreSQL in handling complex queries that will be fired throughout the life cycle of the course design process; (ii) devise an algorithm to determine the completeness of a course design developed using IMODS. This thesis presents the process of creating data model for PostgreSQL and converting it into a graph data model to be abstracted by Neo4j, creating SQL and CYPHER scripts for undertaking experiments on both platforms, testing and elaborate analysis of the results and evaluation of the databases in the context of IMODS.

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Date Created
2017

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Evaluation of instructional module development system

Description

Academia is not what it used to be. In today’s fast-paced world, requirements

are constantly changing, and adapting to these changes in an academic curriculum

can be challenging. Given a specific aspect of a domain, there can be various levels of

proficiency that

Academia is not what it used to be. In today’s fast-paced world, requirements

are constantly changing, and adapting to these changes in an academic curriculum

can be challenging. Given a specific aspect of a domain, there can be various levels of

proficiency that can be achieved by the students. Considering the wide array of needs,

diverse groups need customized course curriculum. The need for having an archetype

to design a course focusing on the outcomes paved the way for Outcome-based

Education (OBE). OBE focuses on the outcomes as opposed to the traditional way of

following a process [23]. According to D. Clark, the major reason for the creation of

Bloom’s taxonomy was not only to stimulate and inspire a higher quality of thinking

in academia – incorporating not just the basic fact-learning and application, but also

to evaluate and analyze on the facts and its applications [7]. Instructional Module

Development System (IMODS) is the culmination of both these models – Bloom’s

Taxonomy and OBE. It is an open-source web-based software that has been

developed on the principles of OBE and Bloom’s Taxonomy. It guides an instructor,

step-by-step, through an outcomes-based process as they define the learning

objectives, the content to be covered and develop an instruction and assessment plan.

The tool also provides the user with a repository of techniques based on the choices

made by them regarding the level of learning while defining the objectives. This helps

in maintaining alignment among all the components of the course design. The tool

also generates documentation to support the course design and provide feedback

when the course is lacking in certain aspects.

It is not just enough to come up with a model that theoretically facilitates

effective result-oriented course design. There should be facts, experiments and proof

that any model succeeds in achieving what it aims to achieve. And thus, there are two

research objectives of this thesis: (i) design a feature for course design feedback and

evaluate its effectiveness; (ii) evaluate the usefulness of a tool like IMODS on various

aspects – (a) the effectiveness of the tool in educating instructors on OBE; (b) the

effectiveness of the tool in providing appropriate and efficient pedagogy and

assessment techniques; (c) the effectiveness of the tool in building the learning

objectives; (d) effectiveness of the tool in document generation; (e) Usability of the

tool; (f) the effectiveness of OBE on course design and expected student outcomes.

The thesis presents a detailed algorithm for course design feedback, its pseudocode, a

description and proof of the correctness of the feature, methods used for evaluation

of the tool, experiments for evaluation and analysis of the obtained results.

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Date Created
2018

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Kitsune: Structurally-Aware and Adaptable Plagiarism Detection

Description

Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. There are a

Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. There are a number of plagiarism detection tools that attempt to encode knowledge about the programming languages they support in order to better detect obscured duplicates. Many such tools do not support a large number of languages because doing so requires too much code and therefore too much maintenance. It is also difficult to add support for new languages because each language is vastly different syntactically. Tools that are more extensible often do so by reducing the features of a language that are encoded and end up closer to text comparison tools than structurally-aware program analysis tools.

Kitsune attempts to remedy these issues by tying itself to Antlr, a pre-existing language recognition tool with over 200 currently supported languages. In addition, it provides an interface through which generic manipulations can be applied to the parse tree generated by Antlr. As Kitsune relies on language-agnostic structure modifications, it can be adapted with minimal effort to provide plagiarism detection for new languages. Kitsune has been evaluated for 10 of the languages in the Antlr grammar repository with success and could easily be extended to support all of the grammars currently developed by Antlr or future grammars which are developed as new languages are written.

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Date Created
2020

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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

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.

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Date Created
2021

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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

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.

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Agent

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Date Created
2021