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Description
This study empirically evaluated the effectiveness of the instructional design, learning tools, and role of the teacher in three versions of a semester-long, high-school remedial Algebra I course to determine what impact self-regulated learning skills and learning pattern training have on students' self-regulation, math achievement, and motivation. The 1st version was a business-as-usual traditional classroom teaching mathematics with direct instruction. The 2rd version of the course provided students with self-paced, individualized Algebra instruction with a web-based, intelligent tutor. The 3rd version of the course coupled self-paced, individualized instruction on the web-based, intelligent Algebra tutor coupled with a series of e-learning modules on self-regulated learning knowledge and skills that were distributed throughout the semester. A quasi-experimental, mixed methods evaluation design was used by assigning pre-registered, high-school remedial Algebra I class periods made up of an approximately equal number of students to one of the three study conditions or course versions: (a) the control course design, (b) web-based, intelligent tutor only course design, and (c) web-based, intelligent tutor + SRL e-learning modules course design. While no statistically significant differences on SRL skills, math achievement or motivation were found between the three conditions, effect-size estimates provide suggestive evidence that using the SRL e-learning modules based on ARCS motivation model (Keller, 2010) and Let Me Learn learning pattern instruction (Dawkins, Kottkamp, & Johnston, 2010) may help students regulate their learning and improve their study skills while using a web-based, intelligent Algebra tutor as evidenced by positive impacts on math achievement, motivation, and self-regulated learning skills. The study also explored predictive analyses using multiple regression and found that predictive models based on independent variables aligned to student demographics, learning mastery skills, and ARCS motivational factors are helpful in defining how to further refine course design and design learning evaluations that measure achievement, motivation, and self-regulated learning in web-based learning environments, including intelligent tutoring systems.
ContributorsBarrus, Angela (Author) / Atkinson, Robert K (Thesis advisor) / Van de Sande, Carla (Committee member) / Savenye, Wilhelmina (Committee member) / Arizona State University (Publisher)
Created2013
Description
Researchers have postulated that math academic achievement increases student success in college (Lee, 2012; Silverman & Seidman, 2011; Vigdor, 2013), yet 80% of universities and 98% of community colleges require many of their first-year students to be placed in remedial courses (Bettinger & Long, 2009). Many high school graduates are entering college ill prepared for the rigors of higher education, lacking understanding of basic and important principles (ACT, 2012). The desire to increase academic achievement is a wide held aspiration in education and the idea of adapting instruction to individuals is one approach to accomplish this goal (Lalley & Gentile, 2009a). Frequently, adaptive learning environments rely on a mastery learning approach, it is thought that when students are afforded the opportunity to master the material, deeper and more meaningful learning is likely to occur. Researchers generally agree that the learning environment, the teaching approach, and the students' attributes are all important to understanding the conditions that promote academic achievement (Bandura, 1977; Bloom, 1968; Guskey, 2010; Cassen, Feinstein & Graham, 2008; Changeiywo, Wambugu & Wachanga, 2011; Lee, 2012; Schunk, 1991; Van Dinther, Dochy & Segers, 2011). The present study investigated the role of college students' affective attributes and skills, such as academic competence and academic resilience, in an adaptive mastery-based learning environment on their academic performance, while enrolled in a remedial mathematics course. The results showed that the combined influence of students' affective attributes and academic resilience had a statistically significant effect on students' academic performance. Further, the mastery-based learning environment also had a significant effect on their academic competence and academic performance.
ContributorsFoshee, Cecile Mary (Author) / Atkinson, Robert K (Thesis advisor) / Elliott, Stephen N. (Committee member) / Horan, John (Committee member) / Arizona State University (Publisher)
Created2013
Description
Traditional usability methods in Human-Computer Interaction (HCI) have been extensively used to understand the usability of products. Measurements of user experience (UX) in traditional HCI studies mostly rely on task performance and observable user interactions with the product or services, such as usability tests, contextual inquiry, and subjective self-report data, including questionnaires, interviews, and usability tests. However, these studies fail to directly reflect a user’s psychological involvement and further fail to explain the cognitive processing and the related emotional arousal. Thus, capturing how users think and feel when they are using a product remains a vital challenge of user experience evaluation studies. Conversely, recent research has revealed that sensor-based affect detection technologies, such as eye tracking, electroencephalography (EEG), galvanic skin response (GSR), and facial expression analysis, effectively capture affective states and physiological responses. These methods are efficient indicators of cognitive involvement and emotional arousal and constitute effective strategies for a comprehensive measurement of UX. The literature review shows that the impacts of sensor-based affect detection systems to the UX can be categorized in two groups: (1) confirmatory to validate the results obtained from the traditional usability methods in UX evaluations; and (2) complementary to enhance the findings or provide more precise and valid evidence. Both provided comprehensive findings to uncover the issues related to mental and physiological pathways to enhance the design of product and services. Therefore, this dissertation claims that it can be efficient to integrate sensor-based affect detection technologies to solve the current gaps or weaknesses of traditional usability methods. The dissertation revealed that the multi-sensor-based UX evaluation approach through biometrics tools and software corroborated user experience identified by traditional UX methods during an online purchasing task. The use these systems enhanced the findings and provided more precise and valid evidence to predict the consumer purchasing preferences. Thus, their impact was “complementary” on overall UX evaluation. The dissertation also provided information of the unique contributions of each tool and recommended some ways user experience researchers can combine both sensor-based and traditional UX approaches to explain consumer purchasing preferences.
ContributorsKula, Irfan (Author) / Atkinson, Robert K (Thesis advisor) / Roscoe, Rod D. (Thesis advisor) / Branaghan, Russell J (Committee member) / Arizona State University (Publisher)
Created2018
Description
This dissertation proposes a new set of analytical methods for high dimensional physiological sensors. The methodologies developed in this work were motivated by problems in learning science, but also apply to numerous disciplines where high dimensional signals are present. In the education field, more data is now available from traditional sources and there is an important need for analytical methods to translate this data into improved learning. Affecting Computing which is the study of new techniques that develop systems to recognize and model human emotions is integrating different physiological signals such as electroencephalogram (EEG) and electromyogram (EMG) to detect and model emotions which later can be used to improve these learning systems.
The first contribution proposes an event-crossover (ECO) methodology to analyze performance in learning environments. The methodology is relevant to studies where it is desired to evaluate the relationships between sentinel events in a learning environment and a physiological measurement which is provided in real time.
The second contribution introduces analytical methods to study relationships between multi-dimensional physiological signals and sentinel events in a learning environment. The methodology proposed learns physiological patterns in the form of node activations near time of events using different statistical techniques.
The third contribution addresses the challenge of performance prediction from physiological signals. Features from the sensors which could be computed early in the learning activity were developed for input to a machine learning model. The objective is to predict success or failure of the student in the learning environment early in the activity. EEG was used as the physiological signal to train a pattern recognition algorithm in order to derive meta affective states.
The last contribution introduced a methodology to predict a learner's performance using Bayes Belief Networks (BBNs). Posterior probabilities of latent nodes were used as inputs to a predictive model in real-time as evidence was accumulated in the BBN.
The methodology was applied to data streams from a video game and from a Damage Control Simulator which were used to predict and quantify performance. The proposed methods provide cognitive scientists with new tools to analyze subjects in learning environments.
The first contribution proposes an event-crossover (ECO) methodology to analyze performance in learning environments. The methodology is relevant to studies where it is desired to evaluate the relationships between sentinel events in a learning environment and a physiological measurement which is provided in real time.
The second contribution introduces analytical methods to study relationships between multi-dimensional physiological signals and sentinel events in a learning environment. The methodology proposed learns physiological patterns in the form of node activations near time of events using different statistical techniques.
The third contribution addresses the challenge of performance prediction from physiological signals. Features from the sensors which could be computed early in the learning activity were developed for input to a machine learning model. The objective is to predict success or failure of the student in the learning environment early in the activity. EEG was used as the physiological signal to train a pattern recognition algorithm in order to derive meta affective states.
The last contribution introduced a methodology to predict a learner's performance using Bayes Belief Networks (BBNs). Posterior probabilities of latent nodes were used as inputs to a predictive model in real-time as evidence was accumulated in the BBN.
The methodology was applied to data streams from a video game and from a Damage Control Simulator which were used to predict and quantify performance. The proposed methods provide cognitive scientists with new tools to analyze subjects in learning environments.
ContributorsLujan Moreno, Gustavo A. (Author) / Runger, George C. (Thesis advisor) / Atkinson, Robert K (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Villalobos, Rene (Committee member) / Arizona State University (Publisher)
Created2017
Description
Expectation for college attendance in the United States continues to rise as more jobs require degrees. This study aims to determine how parental expectations affect high school students in their decision to attend college. By examining parental expectations that were placed on current college students prior to and during the application period, we can determine the positive and negative outcomes of these expectations as well as the atmosphere they are creating. To test the hypothesis, an online survey was distributed to current ASU and Barrett, Honors College students regarding their experience with college applications and their parents' influence on their collegiate attendance. A qualitative analysis of the data was conducted in tandem with an analysis of several case studies to determine the results. These data show that parental expectations are having a significant impact on the enrollment of high school students in college programs. With parents placing these expectations on their children, collegiate enrollment will continue to increase. Further studies will be necessary to determine the specific influences these expectations are placing on students.
ContributorsScheller, Sara Matheson (Co-author) / Johnson, Benjamin (Co-author) / Kappes, Janelle (Thesis director) / Fairbanks, Elizabeth (Committee member) / Division of Teacher Preparation (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05