Matching Items (3)
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
This study was conducted to assess the performance of 176 students who received algebra instruction through an online platform presented in one of two experimental conditions to explore the effect of personalized learning paths by comparing it with linearly flowing instruction. The study was designed around eight research questions investigating the effect of personalized learning paths on students’ learning, intrinsic motivation and satisfaction with their experience. Quantitative results were analyzed using Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA) and split-plot ANOVA methods. Additionally, qualitative feedback data were gathered from students and teachers on their experience to better explain the quantitative findings as well as improve understanding of how to effectively design an adaptive personalized learning platform. Quantitative results of the study showed no statistical difference between students assigned to treatments that compared linear and adaptive personalized instructional flows.
The lack of significant differences was explained by two main factors: (a) low usage and (b) platform and content related issues. Low usage may have prevented students from being exposed to the platforms long enough to create a potential for differences between the groups. Additionally, the reasons for low usage may in part be explained by the qualitative findings, which indicated that unmotivated and tired teachers and students were not very enthusiastic about the study because it occurred near the end of school year. Further, computer access was a challenging issue at the school throughout the study. On the other hand, platform and content related issues worked to inhibit the potential beneficial effects of the platforms. The three prominent issues were: (a) the majority of the students found the content boring or difficult, (b) repeated recommendations from the adaptive platform created frustration, and (c) a barely moving progress bar caused disappointment among participants.
The lack of significant differences was explained by two main factors: (a) low usage and (b) platform and content related issues. Low usage may have prevented students from being exposed to the platforms long enough to create a potential for differences between the groups. Additionally, the reasons for low usage may in part be explained by the qualitative findings, which indicated that unmotivated and tired teachers and students were not very enthusiastic about the study because it occurred near the end of school year. Further, computer access was a challenging issue at the school throughout the study. On the other hand, platform and content related issues worked to inhibit the potential beneficial effects of the platforms. The three prominent issues were: (a) the majority of the students found the content boring or difficult, (b) repeated recommendations from the adaptive platform created frustration, and (c) a barely moving progress bar caused disappointment among participants.
ContributorsBicer, Alpay (Author) / Bitter, Gary G. (Thesis advisor) / Buss, Ray R (Committee member) / Legacy, Jane M. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply instructors with biology questions, a semantic network approach was developed for generating open response biology questions. The generated questions were compared to professional authorized questions.
To boost students’ learning experience, adaptive selection was built on the generated questions. Bayesian Knowledge Tracing was used as embedded assessment of the student’s current competence so that a suitable question could be selected based on the student’s previous performance. A between-subjects experiment with 42 participants was performed, where half of the participants studied with adaptive selected questions and the rest studied with mal-adaptive order of questions. Both groups significantly improved their test scores, and the participants in adaptive group registered larger learning gains than participants in the control group.
To explore the possibility of generating rich instructional feedback for machine-generated questions, a question-paragraph mapping task was identified. Given a set of questions and a list of paragraphs for a textbook, the goal of the task was to map the related paragraphs to each question. An algorithm was developed whose performance was comparable to human annotators.
A multiple-choice question with high quality distractors (incorrect answers) can be pedagogically valuable as well as being much easier to grade than open-response questions. Thus, an algorithm was developed to generate good distractors for multiple-choice questions. The machine-generated multiple-choice questions were compared to human-generated questions in terms of three measures: question difficulty, question discrimination and distractor usefulness. By recruiting 200 participants from Amazon Mechanical Turk, it turned out that the two types of questions performed very closely on all the three measures.
To boost students’ learning experience, adaptive selection was built on the generated questions. Bayesian Knowledge Tracing was used as embedded assessment of the student’s current competence so that a suitable question could be selected based on the student’s previous performance. A between-subjects experiment with 42 participants was performed, where half of the participants studied with adaptive selected questions and the rest studied with mal-adaptive order of questions. Both groups significantly improved their test scores, and the participants in adaptive group registered larger learning gains than participants in the control group.
To explore the possibility of generating rich instructional feedback for machine-generated questions, a question-paragraph mapping task was identified. Given a set of questions and a list of paragraphs for a textbook, the goal of the task was to map the related paragraphs to each question. An algorithm was developed whose performance was comparable to human annotators.
A multiple-choice question with high quality distractors (incorrect answers) can be pedagogically valuable as well as being much easier to grade than open-response questions. Thus, an algorithm was developed to generate good distractors for multiple-choice questions. The machine-generated multiple-choice questions were compared to human-generated questions in terms of three measures: question difficulty, question discrimination and distractor usefulness. By recruiting 200 participants from Amazon Mechanical Turk, it turned out that the two types of questions performed very closely on all the three measures.
ContributorsZhang, Lishang (Author) / VanLehn, Kurt (Thesis advisor) / Baral, Chitta (Committee member) / Hsiao, Ihan (Committee member) / Wright, Christian (Committee member) / Arizona State University (Publisher)
Created2015