ALEKS Constructs as Predictors of High School Mathematics Achievement for Struggling Students

Educators in the United States (U.S.) are increasingly turning to intelligent tutoring systems (ITS) to provide differentiated math instruction to high school students. However, many struggling high school learners do not perform well on these platforms, which reinforces the need for more awareness about effective supports that influence the achievement of learners in these milieus. The purpose of this study was to determine what factors of the Assessment and Learning in Knowledge Spaces (ALEKS), an ITS, are predictive of struggling learners\u27 performance in a blended-learning Algebra 1 course at an inner city technical high school located in the northeastern U.S. The theoretical framework consisted of knowledge base theory, the zone of proximal development, and cognitive learning theory. Three variables (student retention, engagement time, and the ratio of topics mastered to topics practiced) were used to predict the degree of association on the criterion variable (mathematics competencies), as measured by final course progress grades in algebra, and the Preliminary Scholastic Assessment Test (PSATm) math scores. A correlational predictive design was applied to assess the data of a purposive sample of 265 struggling students at the study site; multiple regression analysis was also used to investigate the predictability of these variables. Findings suggest that engagement time and the ratio of mastered to practiced topics were significant predictors of final course progress grades. Nevertheless, these factors were not significant contributors in predicting PSATm score. Retention was identified as the only statistically significant predictor of PSATm score. The results offer educators with additional insights that can facilitate improvements in mathematical content knowledge and promote higher graduation rates for struggling learners in high school mathematics

Relationships Between High School Students\u27 Performance in ALEKS Placement, Preparation, and Learning (PPL) Modules and Performance on the ALEKS College Mathematics Placement Exam

The misalignment between the mathematics taught in high school and the mathematics expected at colleges and universities has created a difficult transition for high school students in the U.S. from high school to college level mathematics. Intelligent Tutoring Systems (ITS) have been used to help high school students transition from the mathematics taught in high school to the mathematics expected at colleges and universities across the country. The purpose of this study was to examine relationships between high school students’ performance in Assessment and LEarning in Knowledge Spaces (ALEKS) Placement, Preparation, and Learning (PPL) Modules and performance on the ALEKS College Mathematics Placement Exam. This study used a quasi-experimental nonequivalent research design. The participants in this study were 100 students from five high schools in a single school district. Students were in two groups: the ALEKS Group, who completed the ALEKS PreCalculus Learning Modules, and the Non-ALEKS Group, who did not complete the modules. The analysis included a 2x2 mixed ANOVA to measure how assignment of the modules affected exam scores. A logistic regression was used to assess differences between the two groups in placing into college algebra. A multiple linear regression was used to identify factors that influenced growth on the ALEKS Mathematics Placement Exam. There was a statistically significant difference in exam scores between the ALEKS Group and the Non-ALEKS Group which indicated that assignment to the ALEKS PPL PreCalculus Learning Modules did increase performance on the ALEKS College Mathematics Placement Exam. Conversely, assignment to the ALEKS PPL PreCalculus Modules did not increase students’ likelihood of placing into College Algebra. The factors that influenced student outcomes on the ALEKS Mathematics Placement exam for those students assigned to the ALEKS Group included the amount of time spent taking the exam in May and the number of modules mastered. These results show that schools could implement ITS into their current mathematics classrooms and help students increase their scores on the ALEKS Mathematics Placement Exam, which has the potential to decrease the number of remedial courses students need to take in college

Cognitive tutoring and assessment systems and mathematics achievement: a quantitative study of the Summit Learning Platform

Title from PDF of title page viewed June 16, 2021Dissertation advisor: Loyce CaruthersVitaIncludes bibliographical references (pages 151-191)Thesis (Ed.D.)--School of Education. University of Missouri--Kansas City, 2021The purpose of this study was to determine if the Summit Learning Platform, a type of Intelligent Tutoring System, has a positive association with mathematics achievement of high school students in grades nine through eleven. The study was conducted in a Midwest suburban school district among three high schools within the same district. Further, a quasi-experimental research design was used with a sample size of 2000 students in the control group and 450 students in the treatment group. Data were compiled from the 2018-2019 school year and applied a combination of t-tests and analysis of variance (ANOVA) to compare the mean scores of the two groups. As comparison points, the Northwest Evaluation Association (NWEA), pre-ACT, and ACT were used in this Midwest district as measures among all students. The results demonstrated that students using the Summit Learning Platform showed significant gains when using their pre-test and post-test scores, but there was not statistical significance when analyzing the measures between the control and treatment groups. As more school districts utilize technological tools in far-reaching efforts to raise achievement levels in math, the intent of the study was to demonstrate potential benefits of the Summit Learning Platform for districts across the nation.Introduction -- Review of literature -- Methodology -- Results of analyses and conclusions -- Discussio

Can Portable EEG Headsets be Used to Determine if Students are Learning?

This study examined EEGs recorded from a single-channel, portable EEG headset (NeuroSky MindWave) during the study period of a paired-associate word paradigm which used Swahili words and their English meanings. It was hypothesized that there would be a significant difference in gamma, theta, and beta band powers for when students recalled words correctly vs. when they did not recall correctly on a subsequent test. There were 35 participants who consisted of students that volunteered at the University of Memphis (20 females and 15 males, 31 of which were right-handed and 4 which were left-handed). A paired-samples t-test suggested that there was a higher mean z-score for brainwave activity during the study period in the high gamma range (41-49.75Hz) for when participants did not recall words correctly on a test, which was opposite of what previous research has found regarding encoding. Based on the results of this study, the MindWave seems to capture muscle activity and/or saccadic behavior that is suggested by higher gamma maximums on average in the study period for word-pairs which resulted in failed recall. Exploratory results may lend insight to future work using portable EEG devices. This study\u27s main objective was to determine if portable EEG devices could be used to determine when students learn new information. Further testing, especially using other portable EEG devices is necessary to answer this question

인공지능 기반 교육 플랫폼 사용에 대한 중국 교사의 인식

학위논문 (석사) -- 서울대학교 대학원 : 사범대학 교육학과, 2021. 2. 조영환.최근 교육 분야에서 인공지능(AI)의 도입이 큰 관심을 끌고 있다. 특히 AI 기술과 학습 분석이 결합한 인공지능 기반 교육 플랫폼은 지금껏 실현되기 어려웠던 맞춤형 학습(personalized learning)과 적응적 학습(adaptive learning)에 도움이 될 수 있도록 발전하고 있다. 인공지능 기반 교육 플랫폼(AI-based education platform)은 학습자의 행동 추적 등을 통해 이들의 특성을 분석하고 진단을 제공한 뒤 분석 결과를 토대로 학습자에게 인지 수준에 맞는 맞춤형 학습자원과 피드백을 제공한다. 인공지능 기반 교육 플랫폼은 교사와 학생에게 실시간 학습 데이터와 분석 결과, 그리고 피드백을 제공할 수 있어 인공지능 기반 교육 플랫폼이 맞춤형 학습에 긍정적인 의미가 있다는 선행 연구도 있었다. 그럼에도 불구하고, 기존 연구는 모델 개발의 차원에서나 엄밀한 실험실 환경에서 인공지능 기반 교육 플랫폼의 효과를 연구해왔으며, 인공지능 기반 교육 플랫폼에 대한 교사의 인식과 관련된 연구는 드물었다. 교사는 인공지능 교육 기술의 사용자이기 때문에 인공지능 교육 기술의 교육 도입에 있어 교사들의 인식과 의견은 중요하다. 본 연구는 인공지능 기반 교육 플랫폼을 활용하는 것에 대한 교사들의 인식을 탐구하였다. 아래 연구 문제를 다루기 위해 질적 연구를 시행하였다. 첫째, 중국 교사들은 인공지능 기반 교육 플랫폼이 중학교 교육에 활용 있어 어떠한 장점이 있다고 인식하는가? 둘째, 중국 교사들은 인공지능 기반 교육 플랫폼과 중학교 교수 활동 요소 간 어떠한 모순이 있다고 인식하는가? 셋째, 중국 교사들은 인공지능 기반 교육 플랫폼을 중학교 교육에 도입할 때 무엇이 필요하다고 인식하는가? 본 연구는 중국 교사들을 연구대상으로 온라인 심층 면담을 하였다. 문헌 리뷰를 통해 면담 질문지를 설계하되 눈덩이표집법 (snowball sampling)을 통해 중국 중학교 교사 14명을 연구참여자로 선정하였다. 선정된 교사들은 모두 인공지능 기반 교육 플랫폼 사용 경험이 있으며 각 교사를 대상으로 약 1시간 정도 면담을 진행하고 녹음하였다. 면담이 끝난 후 녹음 내용을 전사하였으며, 주제분석을 사용하여 면담 내용을 초기 코드 생성하고 면담 자료 속에서 주제를 도출하였다. 특히 연구 문제 2번의 경우, 인공지능 기반 교육 플랫폼 활용과 교수 학습활동 내 여러 요소 간의 모순을 분석하기 위해 활동이론을 연구의 틀로 이용하였다. 최종적으로 연구문제 1에 대한 주제 4개, 연구문제 2에 대한 주제 6개, 연구문제 3에 대한 주제 4개를 도출하였다. 연구 결과로 교사들은 인공지능 기반 교육 플랫폼의 장점에 대해 즉각적인 피드백 제공, 교수학습 지원, 교사의 업무량 감소 등으로 인식하였고, 인공지능 기반 교육 플랫폼이 다양한 교수학습 자원을 통합할 수 있다고 인식하였다. 아울러 교사들은 인공지능 기반 교육 플랫폼의 사용에 있어 기존의 교수학습 활동과 상충된 부분이 있다는 점을 인식하였다. 교사들은 기존 인공지능 기반 교육 플랫폼의 추천 모델이 차별화된 학생들에게 잘 적용되지 못한다는 것을 인식하였다. 그리고 기존 인공지능 기반 교육 플랫폼이 다양한 학습 자원을 잘 분류되지 못하기 때문에 교사들이 사용하기 불편하다. 인공지능 기반 교육 플랫폼을 이용할 때 교사의 지적재산권을 보호하기 위한 명확한 규제가 부족하다고 인식하였다. 이와 함께 학부모들은 인공지능 기반 교육 플랫폼을 사용함으로써 발생할 수 있는 학습자의 인터넷 남용과 시력 저하 문제를 우려하였다. 또 중국의 사회문화적 배경과 교육 특성으로 인해 인공지능 기반 교육 플랫폼을 활용하는 데 학생들의 글씨 쓰기 능력에 영향을 미칠 수 있으며, 학교 내 전자기기 사용 제한도 데이터 수집의 지속성과 효율성에 영향을 미칠 수 있다고 인식하였다. 교사들은 위의 문제들이 인공지능 교육 플랫폼 사용에 대한 규칙 마련과 인공지능 기술을 개선함으로써 완화될 수 있다고 인식하였다. 또한 교사의 실제 요구에 맞게 개발될 수 있도록 인공지능 기반 교육 플랫폼 개발 과정에 교육 전문가와 교사가 참여할 필요가 있다. 본 연구는 중국 교사들이 인공지능 기반 교육 플랫폼에 대한 인식을 탐색하였으며, 인공지능 기반 교육 플랫폼이 교수학습에서의 장점과 문제점을 밝혔다. 아울러 본 연구는 인공지능 기반 교육 플랫폼이 교육 분야에 대규모로 도입될 수 있도록 규칙, 인공지능 기술, 그리고 교육 공학의 차원에서 사용 규범과 기술 개선을 제안하였다. 본 연구를 통해 탐색한 내용이 향후 교육 분야의 인공지능 기반 교육 플랫폼 도입에 활용된다면 인공지능 교육 기술에 관한 연구의 발전에도 기여할 수 있을 것으로 기대된다.In recent years, the introduction of artificial intelligence (AI) in education has attracted widespread attention. In particular, the AI-based education platform based on the combination of AI technology and learning analysis brings new light to the long-standing difficulties in personalized learning and adaptive learning. The AI-based education platform analyzes learners' characteristics by collecting their data and tracking their learning behavior. It then generates cognitive diagnosis for learners and provides them with personalized learning resources and adaptive feedback that match their cognitive level based on systematic analysis. With the help of the AI-based education platform, teachers and students can get real-time educational data and analysis result，as well as the feedback and treatment corresponding to the results. Previous studies have already demonstrated and proved its positive significance to personalized learning. However, these studies mostly start from a model development perspective or in a rigorous laboratory environment. There has been little research on teachers' perceptions of AI-based education platform. As a direct user of AI educational technologies, teachers' perceptions and suggestions are vital for introducing AIEd in education. In this study, the researcher explored teachers' perceptions of using AI-based education platform in teaching. The study conducted qualitative research to address the following research questions: 1) How do Chinese teachers perceive the advantages of AI-based education platforms for teaching and learning in secondary school? 2) How do Chinese teachers perceive the contradictions between AI-based education platforms and the secondary school system? 3）How do Chinese teachers suggest applying AI-based education platforms in secondary school? And it referred to the in-depth online interview with Chinese teachers who had experience with AI-based education platform. Interview questions were constructed through the literature review, and 14 secondary school teachers were selected by the snowball sampling method. The interviews lasted for an average of one hour per teacher and were transcribed from the audio recordings to text documents when finished. Afterward, the data were analyzed using thematic analysis, including generating initial codes, searching and reviewing the categories, and deriving the themes finally. Notably, for research question two, the researcher used the activity theory framework to analyze the contradictions among the use of the AI-based education platform and the various elements of the teaching and learning activities. Finally, four themes for research question 1, six themes for research question 2, and four themes for research question 3 were derived. As for the advantages, teachers believe that AI-based education platforms can provide instant feedback, targeted and systematic teaching support, and reduce teachers' workload. At the same time, AI-based education platforms can also integrate teaching resources in different areas. Teachers also recognized that the AI-based education platforms might trigger contradictions in existing teaching activities. They are aware of the situation that the recommended model of the AI-based education platform is not suitable for all levels of students; that a large number of learning resources are not classified properly enough to meet the needs of teachers, and that there lack clear rules and regulations to protect teachers' intellectual property rights when using the platform. Besides, parents are also concerned about the potential risk of internet addiction and vision problems using AI-based education platforms. Moreover, the use of the AI-based education platform may also affect students' ability to write Chinese characters due to the socio-historical background and educational characteristics in China. Furthermore, the restricted use of electronic devices on campus may also impact the consistent and effective education data collection. Teachers believe that these problems can be solved by improving rules and AI technology. Moreover, to make the platform more in line with the actual teaching requirements, teachers and education experts can also be involved in the development process of AI-based education platform. This study explored how Chinese teachers perceive the AI-based education platform and found that the AI-based education platform was conducive to personalized teaching and learning. At the same time, this study put forward some suggestions from the perspective of rules, AI technology, and educational technology, hoping to provide a good value for the future large-scale introduction of AI-based education platforms in education.CHAPTER 1. INTRODUCTION 1 1.1. Problem Statement 1 1.2. Purpose of Research 7 1.3. Definition of Terms 8 CHAPTER 2. LITERATURE REVIEW 10 2.1. AI in Education 10 2.1.1 AI for Learning and Teaching 10 2.1.2 AI-based Education Platform 14 2.1.3 Teachers' Perception on AI-based Education Platform 18 2.2. Activity Theory 20 CHAPTER 3. RESEARCH METHOD 23 3.1. Research Design 23 3.2. Participants 25 3.3. Instrumentation 26 3.3.1 Potential Value of AI System in Education 26 3.4. Data Collection 33 3.5. Data Analysis 34 CHAPTER 4. FINDINGS 36 4.1. Advantages of Using AI-based Education Platform 36 4.1.1 Instant Feedback 37 4.1.2 Targeted and Systematic Teaching Support 42 4.1.3 Educational Resources Sharing 46 4.1.4 Reducing Workload 49 4.2. Tensions of Using AI-based Education Platform 51 4.2.1 Inadequately Meet the Needs of Teachers 52 4.2.2 Failure to Satisfy Low and High Achievers 54 4.2.3 Intellectual Property Violation 56 4.2.4 Guardian's Concern 57 4.2.5 School Rules about the Use of Electronic Devices 58 4.2.6 Implication for Chinese Character Education 59 4.3. Suggestion of Using AI-based Education Platform 61 4.3.1 Improving Rules of Using the AI-based Education Platform 61 4.3.2 Improving Rules of Protecting Teachers Right 62 4.3.3 Improving AI Technology 64 4.3.4 Participatory Design 66 CHAPTER 5. DISCUSSION AND CONCLUSION 68 5.1. Discussion 68 5.2. Conclusion 72 REFERENCE 75 APPENDIX 1 98 APPENDIX 2 100 국문초록 112Maste

Computational Psychometrics for Item-based Computerized Adaptive Learning

With advances in computer technology and expanded access to educational data, psychometrics faces new opportunities and challenges for enhancing pattern discovery and decision-making in testing and learning. In this dissertation, I introduced three computational psychometrics studies for solving the technical problems in item-based computerized adaptive learning (CAL) systems related to dynamic measurement, diagnosis, and recommendation based on Bayesian item response theory (IRT). For the first study, I introduced a new knowledge tracing (KT) model, dynamic IRT (DIRT), which can iteratively update the posterior distribution of latent ability based on moment match approximation and capture the uncertainty of ability change during the learning process. For dynamic measurement, DIRT has advantages in interpretation, flexibility, computation cost, and implementability. For the second study, A new measurement model, named multilevel and multidimensional item response theory with Q matrix (MMIRT-Q), was proposed to provide fine-grained diagnostic feedback. I introduced sequential Monte Carlo (SMC) for online estimation of latent abilities. For the third study, I proposed the maximum expected ratio of posterior variance reduction criterion (MERPV) for testing purposes and the maximum expected improvement in posterior mean (MEIPM) criterion for learning purposes under the unified framework of IRT. With these computational psychometrics solutions, we can improve the students’ learning and testing experience with accurate psychometrics measurement, timely diagnosis feedback, and efficient item selection

Instructors as Orchestrators: A Qualitative Case Study that Explores the role of Instructors in an Adaptive Learning Classroom Environment

This qualitative case study highlights the vital role of the instructor and the importance of learning analytics (LA) in an adaptive learning (AL) classroom environment. Existing research does not consistently support a particular outcome for the use of the AL platform, ALEKS, as an instructional approach. An examination of the AL landscape has been characterized by inconsistent and inconclusive results. The persistent emphasis on students’ attitudes and performance in relation to ALEKS, along with the comparative approach to traditionally taught classes, have prevented us from understanding the complex and multifaceted ALEKS classroom environment. This study is novel because it distinguishes itself from previous research by providing the AL landscape with a supplementary lens – that of the instructors. By exploring how the instructors in this study used the LA from ALEKS to enrich the students’ learning experiences, we intended to gain a renewed understanding and a reconceptualization of the instructor’s role from a parameter that can be overlooked to an orchestrator whose presence is vital. This study draws on Laurillard’s Conversational Framework (CF) as a theoretical foundation, which promotes an education-driven approach where technology serves pedagogy, not the other way around. This allows for an evaluation that challenges ALEKS to deliver on what education requires of it, and makes visible the vital role that educators play in the classroom. In sharp contrast with the quantitative orientation that has dominated previous research studies, this study espouses a qualitative stance to inquiry using open-ended research questions. Therefore, this dissertation employs semi-structured interviews, classroom observations, and documentary evidence as data collection methods to paint a vibrant image of the case study under investigation. The findings demonstrate the multi-layered and intricate nature of an AL classroom environment. Evidence suggests that an AL approach to instruction depends on the active and engaged presence of instructors. Using the LA from the AL platform to cater to the individual needs of every student became an integral part of the instructor’s role in the ALEKS classes studied. Further evidence indicates that the LA in ALEKS contributes towards a positive classroom dynamic and enhanced learner-instructor interaction. The CF adds another dimension to the findings and leads to an improved articulation of how the different aspects of learning are enacted in an AL environment. The study concludes with an array of recommendations for implementers and decision-makers with regard to leading a successful AL experience

Did It Change How We Teach? A Qualitative Exploration Into Teacher Perceptions of How Technology Changed in the Classroom as a Result of the Pandemic

The purpose of this case study was to understand the perceived change in educator attitudes from the pandemic concerning technology at Cornerstone School. The problem addressed in this study was the lack of motivation of educators to try new ways to use technology in their classrooms. The theory guiding this study was J. Bruner’s constructivist theory which focuses on obtaining knowledge through discovery. The connection between Bruner’s theory and the perceived change in teachers’ attitudes was that teachers learned technology through their use and discovery. A qualitative case study design was used to carry out this investigation. Ten educators were recruited using a typical purposeful sampling strategy; all were from a small private school. These included educators from the kindergarten through 11th-grade levels. The data were collected through a questionnaire followed by semi-structured individual interviews, an analysis of reflective journal prompts, and an analysis of artifacts. Findings revealed themes pertaining to the study’s purpose, including pre-pandemic attitudes toward technology, successful practices using technology during the pandemic, and post-pandemic attitudes toward technology. Confidence developed by the participants through the discovery of technology used confirmed Bruner’s theory that learning through discovery gave the participants a more positive attitude towards technology and influenced how the participants taught

The Impact of Human Resource Capital on Ethnic Minority Middle School Students' Engagement in a Mathematics Community of Practice and their Mathematics Identities

The purpose of this instrumental case study (Stake, 1995) was to understand (a) the impact of human resource capital on ethnic minority middle school students' engagement in a mathematics community of practice and (b) the impact of this engagement on their mathematics identities. Case study methodology was used to collect data from nine participants. Qualitative data collection methods were used, including observations, audio taped individual and focus group interviews of participants, participants' responses to mathematics scenarios and a thorough examination of weekly journal logs kept by the participants. Data were analyzed using Creswell's (2003) content analysis coding procedures. The two themes that emerged from analysis of the data were sociocultural capital and personal capital. Sociocultural capital of the participants was demonstrated through their interactions as a mathematics community of learners including collaboration with peers, communication with peers, and the sense of community the participants perceived within the mathematics classroom [community of practice]. Other indicators of sociocultural capital in this study were participants' social networks with their peers and family members as well as their views of the relevance of mathematics in extracurricular activities and mathematics-related careers. Participants' personal capital was demonstrated by their intrinsic motivation, including factors of self motivation and self regulation; and by participants' extrinsic motivation, which includes the expectations of participants' families, participants' individual career goals and their prior knowledge and experiences. Implications for relevant classroom practices and further research are recommended