Improving School Improvement

PREFACEIn opening this volume, you might be thinking:Is another book on school improvement really needed?Clearly our answer is yes. Our analyses of prevailing school improvement legislation, planning, and literature indicates fundamental deficiencies, especially with respect to enhancing equity of opportunity and closing the achievement gap.Here is what our work uniquely brings to policy and planning tables:(1) An expanded framework for school improvement – We highlight that moving from a two- to a three-component policy and practice framework is essential for closing the opportunity and achievement gaps. (That is, expanding from focusing primarily on instruction and management/government concerns by establishing a third primary component to improve how schools address barriers to learning and teaching.)(2) An emphasis on integrating a deep understanding of motivation – We underscore that concerns about engagement, management of behavior, school climate, equity of opportunity, and student outcomes require an up-to-date grasp of motivation and especially intrinsic motivation.(3) Clarification of the nature and scope of personalized teaching – We define personalization as the process of matching learner motivation and capabilities and stress that it is the learner's perception that determines whether the match is a good one.(4) A reframing of remediation and special education – We formulate these processes as personalized special assistance that is applied in and out of classrooms and practiced in a sequential and hierarchical manner.(5) A prototype for transforming student and learning supports – We provide a framework for a unified, comprehensive, and equitable system designed to address barriers to learning and teaching and re-engage disconnected students and families.(6) A reworking of the leadership structure for whole school improvement --We outline how the operational infrastructure can and must be realigned in keeping with a three component school improvement framework.(7) A systemic approach to enhancing school-community collaboration – We delineate a leadership role for schools in outreaching to communities in order to work on shared concerns through a formal collaborative operational infrastructure that enables weaving together resources to advance the work.(8) An expanded framework for school accountability – We reframe school accountability to ensure a balanced approach that accounts for a shift to a three component school improvement policy.(9) Guidance for substantive, scalable, and sustainable systemic changes –We frame mechanisms and discuss lessons learned related to facilitating fundamental systemic changes and replicating and sustaining them across a district.The frameworks and practices presented are based on our many years of work in schools and from efforts to enhance school-community collaboration. We incorporate insights from various theories and the large body of relevant research and from lessons learned and shared by many school leaders and staff who strive everyday to do their best for children.Our emphasis on new directions in no way is meant to demean current efforts. We know that the demands placed on those working in schools go well beyond what anyone should be asked to do. Given the current working conditions in many schools, our intent is to help make the hard work generate better results. To this end, we highlight new directions and systemic pathways for improving school outcomes.Some of what we propose is difficult to accomplish. Hopefully, the fact that there are schools, districts, and state agencies already trailblazing the way will engender a sense of hope and encouragement to those committed to innovation.It will be obvious that our work owes much to many. We are especially grateful to those who are pioneering major systemic changes across the country. These leaders and so many in the field have generously offered their insights and wisdom. And, of course, we are indebted to hundreds of scholars whose research and writing is a shared treasure. As always, we take this opportunity to thank Perry Nelson and the host of graduate and undergraduate students at UCLA who contribute so much to our work each day, and to the many young people and their families who continue to teach us all.Respectfully submitted for your consideration,Howard Adelman & Linda Taylo

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

Enhancing Students' Metacognition via AI-Driven Educational Support Systems

As the penetration of digital technology deepens and the demands for educational modernization grow, attention is increasingly being drawn towards the application of artificial intelligence (AI) in the field of education. Especially in educational practice, the optimization of students’ learning experiences and the enhancement of their metacognitive abilities through AI technology have captivated the interest of numerous educators and scholars. Metacognition, which represents a core skill in student self-regulation and self-management, has a significant impact on student learning outcomes and quality. However, current educational support systems primarily rely upon traditional methods of data collection and analysis, which have limitations in terms of real-time responsiveness, granularity, and comprehensiveness. The present research aims to investigate the integration of AI technology with a specific focus on the learning process through educational support systems and the development of a cooperative teaching interaction model. This will ultimately enhance the development of students’ metacognitive abilities more effectively

The effect of personalised system of instruction on technical college students’ achievement in basic electricity

Abstracts in English and Northern SothoThe study determined the effect of a personalised system of instruction (PSI) teaching model on students’ achievement in Basic Electricity in technical colleges. The behaviourism, social cognitive, and mastery learning theories framed the study. The study employed a pre-test/post-test non-equivalent control group, and a non-randomised, quasi-experimental design with a 2 x 2 x 2 factorial matrix in which the treatment operates at two levels crossed with gender and school location. The population of the study consisted of all year-two students of engineering-related trades in all the technical colleges in Osun State, Nigeria. Using the purposive sampling technique, four technical colleges with n = 152 Basic Electricity students and four teachers participated in the study. Two schools (one urban and one rural) of which urban n = 57 and rural n = 30, formed the experimental group (n = 87), and the other two schools (one urban with n = 39 and one rural with n = 26) formed the control group (n = 65). The personalised system of instruction was implemented in the two experimental schools by the respective Basic Electricity teachers who, as research assistants, had been trained on the use of the strategies of the personalised system of instruction in teaching Basic Electricity. The two control schools were also taught by their teachers using the conventional lecture method (CLM). Intact classes participated in the study, as it was not possible to randomly select participants for the study. The study lasted six weeks (one week for training and five weeks for the main study). Seven research questions and fourteen research hypotheses were raised, answered, and tested in the study. The quantitative data were collected using standardised achievement tests in Basic Electricity and a Students’ Attitude to Basic Electricity questionnaire. The instruments used were pilot-tested and the results were computed using the Kuder-Richardson (K-R) 20 formula and Cronbach-alpha, which gave r = 0.937 for the Basic Electricity achievement tests and α = 0.952 for the Students’ Attitude to Basic Electricity questionnaires. Data collected from the study were analysed using the frequency count, percentage, bar chart and mean for the research questions and the Analysis of Covariance (ANCOVA) for the hypotheses tested at the 0.05 level of significance. The results of this study indicated that the students in the experimental group performed significantly better than their counterparts in the control group in the Basic Electricity achievement scores. Male students taught Basic Electricity using PSI and CLM performed better than their female counterparts. Students from schools located in the urban area taught Basic Electricity using PSI and CLM performed better in the mean achievement scores than students from schools located in the rural area. The experimental group had a higher mean attitude score than the control group. Female students taught Basic Electricity using the PSI model had a higher mean attitude score than their male counterparts. Students from the schools located in the urban area taught Basic Electricity using PSI had a higher mean attitude score than their counterparts from the rural schools. There was a statistically highly significant main effect of treatment (PSI) on the achievement of students in Basic Electricity [F (1,143) = 171.937, P < .05, η2 = .546]. There was also a significant main effect of gender on technical college students’ achievement in Basic Electricity [F (1,143) = 43.943, P < .05, η2 = .235]. Furthermore, there was a significant main effect of school location on technical college students’ achievement in Basic Electricity [F (1,143) = 17.581, P < .05, η2 = .109]. There was a significant interaction effect of the personalised system of instruction and school location on technical college students’ learning outcomes in Basic Electricity [F (1,143) = 4.191; P = .042, η2 = .028] and the interaction was ordinal, showing that the interaction effect was stronger with students taught Basic Electricity using the PSI. There was a highly significant effect of the personalised system of instruction on the attitude of students to Basic Electricity, [F (1,143) = 11.863, P < .05, η2 = .077]. Therefore, it is strongly recommended that Basic Electricity teachers use PSI instructional strategies in their classes to facilitate students’ performance. Basic Electricity teachers should re-assess their classroom instructional practice to accommodate a shift from an instructional practice that makes learners passive listeners, to a practice that engages learners actively in the instructional processes. Furthermore, technical teachers should also be exposed to different enhancement strategies that will assist them in taking care of individual differences between students in the classroom. This will help students develop positive attitudes towards Basic Electricity, have self-confidence, and be more positively disposed towards obtaining good results in Basic Electricity. There should be adequate provision of the basic facilities necessary for the effective implementation of a PSI approach to teaching and learning Basic Electricity. As indicated, PSI could also positively influence the students’ attitude toward Basic Electricity. There is a need for teachers to be knowledgeable about the PSI learning strategy before it can be implemented in the classroom and, therefore, they should regularly attend conferences, workshops and seminars, where they can learn the requisite skills and knowledge to handle this innovative teaching strategy.Nyakisiso e laeditse khuetso ya mokgwa wo o hlaotswego wa mmotlolo wa thuto wa go ruta (PSI) go phihlelelo ya baithuti ba mo go Mohlagase wa Motheo ka dikholetseng tsa setegeniki. Diteori tsa tlwaetso, tshomiso ya tshedimoso, le leano la go ithuta di dikologile nyakisiso. Nyakisiso e somisitse sehlopha sa taolo ye e sa lekanego ya teko ya pele/teko ya ka morago, le ya go se kgethe, moakanyetso wa boitekelo bja tlholo ya khuetso ka factorial matrix wa 2 x 2 x 2 woo ka gare ga wona o somago tshwaro maemong a mabedi a go kgabaganywa ka bong le lefelo la sekolo. Palo ya batho ba ba lego mo nyakisisong e na le baithuti ba ngwaga wa bobedi ka moka ba mesomo ya go amana le boentseneere ka dikholetseng ka moka tsa setegeniki ka Osun State, Nigeria. Ka go somisa thekniki ya go dira sampole ka go kgetha, dikholetse tsa setegeniki tse nne tsa go ba le n = 152 ya baithuti ba Mohlagase wa Motheo gomme barutisi ba bane ba kgathile tema ka nyakisisong. Dikolo tse pedi (se setee sa motsesetoropo gomme se setee sa motsemagae) tseo go tsona n = 57 e lego ya motsesetoropo gape le ya motsemagae n = 30, di bopile sehlopha sa boitekelo (n = 87), gomme dikolo tse dingwe tse pedi (se setee sa motsesetoropo sa go ba le = 39 le se setee sa go ba le n = 26) di bopile sehlopha sa taolo (n = 65). Mokgwa wo o hlaotswego wa tlhahlo o phethagaditswe ka dikolong tse pedi tsa boitekelo ka barutisi ba Mohlagase wa Motheo ba go fapana bao, bjalo ka bathusi ba nyakisiso, ba hlahletswego tshomiso ya maano a mokgwa wo o hlaotswego wa tlhahlo mo go ruteng Mohlagase wa Motheo. Dikolo tse pedi tsa taolo le tsona di rutilwe ke barutisi ba tsona ka go somisa mokgwa wa go ruta wa tlwaelo (CLM). Dihlopha tsa baithuti ka moka tse di kgathilego tema ka gare ga nyakisiso, ka ge go sa kgonege go kgetha ka sewelo bakgathatema ba nyakisiso. Nyakisiso e dirilwe ka dibeke tse tshela (beke ye tee ya tlhahlo le dibeke tse tlhano tsa nyakisiso ye kgolo). Haephothesese le dipotsiso tsa nyakisiso tse lesomenne di botsisitswe, di arabilwe, ebile di lekilwe ka gare ga nyakisiso. Datha ya khwalithethifi e kgobokeditswe ka go somisa diteko tsa phihlelelo tse di lekaneditswego mo go Mohlagase wa Motheo le Maikutlo a Baithuti go lenaneopotsiso la Mohlagase wa Motheo. Didiriswa tse di somisitswego di dirilwe teko pele gomme dipoelo di tsentswe ka khomphutheng ka go somisa Kuder-Richardson (K-R) fomula ya 20 le Cronbach-alpha, yeo e filego r = 0.937 ya diteko tsa phihlelelo ya Mohlagase wa Motheo le α = 0.952 ya Maikutlo a Baithuti go mananeopotsiso a Mohlagase wa Motheo. Datha ye e kgobokeditswego go tswa nyakisisong e sekasekilwe ka go somisa palelo ya boipoeletso, phesente, tshate ya paa le palogare ya dipotsiso tsa nyakisiso le Tshekatsheko ya Kelo ya Phapano (ANCOVA) ya haephothesese ye e lekilwego maemong a 0.05 a bohlokwa. Dipoelo tsa nyakisiso ye di laeditse gore baithuti ka sehlopheng sa boitekelo ba somile bokaone kudu go feta dithaka tsa bona ka sehlopheng sa taolo ka gare ga dintlha tsa phihlelelo ya Mohlagase wa Motheo. Baithuti ba banna ba rutile Mohlagase wa Motheo ba somisa PSI gomme ba CLM ba somile bokaone go feta dithaka tsa bona tsa basadi. Baithuti ba go tswa dikolong tse di lego ka motsesetoropong ba rutile Mohlagase wa Motheo ba somisa PSI gomme ba CLM ba somile bokaone mo go dintlha tsa phihlelelo tsa palogare go feta baithuti ba go tswa dikolong tsa ka metsemagaeng. Sehlopha sa boitekelo se bile le ntlha ya maikutlo ya palogare ya godingwana go feta dithaka tsa bona tsa basadi. Baithuti ba go tswa dikolong tsa ka motsesetoropong ba rutile Mohlagase wa Motheo ba somisa PSI ba bile le ntlha ya maikutlo ya palogare ya godingwana go feta dithaka tsa bona tsa go tswa dikolong tsa motsemagae. Go ya ka dipalopalo go bile le khuetso ye kgolo ye bohlokwa ya godimo ya tshwaro (PSI) go phihlelelo ya baithuti mo go Mohlagase wa Motheo [F (1,143) = 171.937, P < .05, η2 = .546]. Gape go bile le khuetso ye kgolo ye bohlokwa ya bong godimo ga phihlelelo ya baithuti ba kholetse ya setegeniki mo go Mohlagase wa Motheo [F (1,143) = 43.943, P < .05, η2 = .235]. Se sengwe gape, go bile le khuetso ye kgolo ya bohlokwa ya lefelo la sekolo godimo ga phihlelelo ya baithuti ba kholetse ya setegeniki mo go Mohlagase wa Motheo [F (1,143) = 17.581, P < .05, η2 = .109]. Go bile le khuetso ya kopantsho ye bohlokwa ya mokgwa wo o hlaotswego wa tlhahlo le lefelo la sekolo godimo ga dipoelo tsa thuto tsa baithuti ba kholetse ba setegeniki mo go Mohlagase wa Motheo [F (1,143) = 4.191; P = .042, η2 = .028] gomme kopantsho e be e le ya tatelano, e laetsa gore khuetso ya tsenelelano e be e le maatla ka baithuti ba go ruta Mohlagase wa Motheo ba somisa PSI. Go bile le khuetso ye bohlokwa ya godimo ya mokgwa wo o hlaotswego wa tlhahlo godimo ga maikutlo a baithuti go Mohlagase wa Motheo, [F (1,143) = 11.863, P < .05, η2 = .077]. Fela, go sisinywa gagolo gore barutisi ba Mohlagase wa Motheo ba somise maano a tlhahlo a PSI ka dihlopeng tsa bona tsa baithuti go kgontsha phefomentshe ya baithuti. Barutisi ba Mohlagase wa Motheo ba swanela go lekolaleswa tiriso ya tlhahlo ka phaposiborutelong bja bona go akaretsa go tloga go tiriso ya tlhahlo yeo e dirago gore baithuti e be batheeletsi ba go se tshwenye, go ya go tiriso yeo e kgathisago baithuti tema ka mafolofolo ka gare ga ditshepetso tsa ditshepetso tsa tlhahlo. Se sengwe gape, barutisi ba setegeniki ba swanela gape go tsebiswa maano a kaonafatso a go fapana ao a tlago ba thusa go hlokomela diphapano gare ga baithuti ka gare ga phaposiborutelo. Se se tla thusa baithuti go ba le maikutlo a mabotse go Mohlagase wa Motheo, ba be le boitshepho, le go itokisetsa gabotse go ya go hwetseng dipoelo tse di botse mo go Mohlagase wa Motheo. Go swanela go ba le kabo ye e lekanego ya ditlabakelo tsa motheo tsa phethagatso ye botse ya mokgwa wa PSI mo go ruteng le go ithuteng Mohlagase wa Motheo. Bjalo ka ge go laeditswe, PSI gape e ka huetsa gabotse maikutlo a baithuti go Mohlagase wa Motheo. Go na le nyakego ya gore barutisi ba amogelege mabapi le leano la go ithuta la PSI pele e ka phethagatswa ka gare ga phaposiborutelo gomme, bjalo, ba swanela go tsenela dikhonferentshe kgafetsa, diwekesopo le diseminare, fao ba kago ithuta mabokgoni bja maleba le go amogela go swaragane le leano le la thuto ya boithomelo.Science and Technology EducationPh. D. (Technology Education

Blended learning for building student-teachers’ capacity to learn and teach science-related interdisciplinary subjects: The case of Hong Kong

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Ethical Practices in Facilitating Learning and Improving Educational Technology and Alternative Education by Using Google Classroom

The globalization period has ushered in a race among nations to develop, adopt, and master new technology. Using technology in education is one of the most effective methods to do this. Utilizing education-based technologies, such as Google Classroom, can help Philippines Woman University (PWU) overcome the barriers to the adoption of online learning. Google Classroom has a distinct look and feel, like many new programs that emerge. Google Classroom is a component of the Google Apps for Education (GAFE) online suite of productivity tools for instructors and learners using the internet. This program offers a centralized location for interacting with students, offering feedback, and assigning assignments. Google Classroom has implications for how education is implemented in Philippines Woman University (PWU), including how teachers are evaluated, how students use technology, and more

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Virtual worlds are 3D environments that provide a feeling of immersion and a high degree of interaction, collaboration, communication between users. Its applicability can be focused on the educational scope, in which theories can be integrated as the basis to didactic activities carried out in the 3D environment, being its area of interdisciplinary comprehension. In this context, this article presents the use of a Virtual World built to assist in the teaching of Science for students of the middle school, whose articulation of the activities performed in the course are based on the precepts of the educational theory Mastery Learning. Tests were carried out in the subject of science, being divided into two periods with different groups for comparative purposes and realized evaluations during the period of the experiments. Kruskal-Wallis and Wilcoxon-Mann-Whitney non-parametric test were applied to the results of the assessments to ascertain the performance of each group. It was verified in the general analyzis that the participants who used the Virtual World had a growing performance, with high medians and adequate distribution of the results, being predominant of a smaller variability and amplitude. Thus, was possible to conclude that the results obtained with the approach were positive, which led to the validation of this research and presented a clear contribution to the academic environment

Towards Designing AI-Enabled Adaptive Learning Systems

Paper I, III, IV and V are not available as a part of the dissertation due to the copyright.Among the many innovations driven by artificial intelligence (AI) are more advanced learning systems known as AI-enabled adaptive learning systems (AI-ALS). AI-ALS are platforms that adapt to the learning strategies of students by modifying the order and difficulty level of learning tasks based on the abilities of students. These systems support adaptive learning, which is the personalization of learning for students in a learning system, such that the system can deal with individual differences in aptitude. AI-ALS are gaining traction due to their ability to deliver learning content and adapt to individual student needs. While the potential and importance of such systems have been well documented, the actual implementation of AI-ALS and other AI-based learning systems in real-world teaching and learning settings has not reached the effectiveness envisaged on the level of theory. Moreover, AI-ALS lack transferable insights and codification of knowledge on their design and development. The reason for this is that many previous studies were experimental. Thus, this dissertation aims to narrow the gap between experimental research and field practice by providing practical design statements that can be implemented in effective AI-ALSs.publishedVersio