Behavioral Pattern Mining and Modeling in Programming Problem Solving

abstract: Online learning platforms such as massive online open courses (MOOCs) and intelligent tutoring systems (ITSs) have made learning more accessible and personalized. These systems generate unprecedented amounts of behavioral data and open the way for predicting students’ future performance based on their behavior, and for assessing their strengths and weaknesses in learning. This thesis attempts to mine students’ working patterns using a programming problem solving system, and build predictive models to estimate students’ learning. QuizIT, a programming solving system, was used to collect students’ problem-solving activities from a lower-division computer science programming course in 2016 Fall semester. Differential mining techniques were used to extract frequent patterns based on each activity provided details about question’s correctness, complexity, topic, and time to represent students’ behavior. These patterns were further used to build classifiers to predict students’ performances. Seven main learning behaviors were discovered based on these patterns, which provided insight into students’ metacognitive skills and thought processes. Besides predicting students’ performance group, the classification models also helped in finding important behaviors which were crucial in determining a student’s positive or negative performance throughout the semester.Dissertation/ThesisMasters Thesis Computer Science 201

Summer 2013

The Official Community Newsletter for Collin College Connection is the college\u27s award-winning community newsletter published three times a year to inform members of the Collin County community about college news, awards, events, and more.https://digitalcommons.collin.edu/connection/1040/thumbnail.jp

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

The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parametrerized Exercises

ABSTRACT Parameterized exercises have recently emerged as an important tool for online assessment and learning. The ability to generate multiple versions of the same exercise with different parameters helps to support learning-by-doing and decreases cheating during assessment. At the same time, our experience with using parameterized exercises for Java programming reveals suboptimal use of this technology as demonstrated by repeated successful and failed attempts to solve the same problem. In this paper we present the results of our work on modeling and examining patterns of student behavior with parameterized exercises using Problem Solving Genome, a compact encapsulation of individual behavior patterns. We started with micro-patterns (genes) that describe small chunks of repetitive behavior and constructed individual genomes as frequency profiles that shows the dominance of each gene in individual behavior. The exploration of student genomes revealed that individual genome is very stable, distinguishing students from their peers and changing very little with the growth of knowledge over the course. Using the genome, we were able to analyze student behavior on the group level and identify genes associated with good and bad learning performance

Former Students of Hispanic Descent First in the Family to Graduate High School: What Did It Take to Achieve a High School Diploma and Have They Transitioned to College?

This Record of Study was conducted in an effort to explore the academic experiences of Hispanic students first-in-the-family to graduate high school. The goal was to identify who these students were and analyze student performance through the lens of academic achievements such as diploma plans, state assessment performance, and advanced course participation. Student data were collected from existing academic records and interview responses from select participants during the study. Data analyses presented both expected and unexpected outcomes. Graduates who earned Distinguished Achievement Program (DAP) diplomas and those who participated in advanced course studies, overall, performed better on state assessment exams than students who earned a minimum graduation plan diploma. Results from analysis of participant interviews expanded the picture of the first-in-the-family to graduate student experience outside of the extant student achievement data, with one unexpected discovery: Regardless of state test performance or advanced course participation, graduates felt significantly unprepared for the expectations they experienced in higher education

Improving digital ink interpretation through expected type prediction and dynamic dispatch

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 67-70).Interpretation accuracy of current applications dependent on interpretation of handwritten "digital ink" can be improved by providing contextual information about an ink sample's expected type. This expected type, however, has to be known or provided a priori, and poses several challenges if unknown or ambiguous. We have developed a novel approach that uses a classic machine learning technique to predict this expected type from an ink sample. By extracting many relevant features from the ink, and performing generic dimensionality reduction, we can obtain a minimum prediction accuracy of 89% for experiments involving up to five different expected types. With this approach, we can create a "dynamic dispatch interpreter" by biasing interpretation differently according to the predicted expected types of the ink samples. When evaluated in the domain of introductory computer science, our interpreter achieves high interpretation accuracy (87%), an improvement from Microsoft's default interpreter (62%), and comparable with other previous interpreters (87-89%), which, unlike ours, require additional expected type information for each ink sample.by Kah Seng Tay.M.Eng

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