published_or_final_version / Education / Master / Master of Education

The government of the Federal Democratic Republic of Ethiopia proclaimed a new curriculum for reconstructing the education system. The programme aimed at changing the predominantly-used teacher-centred instructional strategies to student-centred, active learning methods. This motivated the main research question of this study namely What are Ethiopian students’ learning styles and attitudes towards active learning approaches? The specific research questions that were investigated were: • What are the learning styles of students in Grade 10 public and private schools and at second year university level, and do these students prefer certain learning styles? • What are the attitudes of students at Grade 10 public and private schools, and at second year university level in respect of active learning approaches? • Do significant relationships exist between the students’ learning styles and their attitudes towards active learning as regards the four dimensions of the Index of Learning Styles (ILS), namely active-reflective, sensing-intuitive, visual-reflective and sequential-global? • Are there significant differences in the students’ learning styles and their attitudes towards active learning in respect of gender, different education levels and types of schools? In order to answer these questions, the study made use of an exploratory, descriptive design. By means of questionnaires data were collected from a purposefully and a conveniently selected sample of 920 students from Grade 10 government and private schools and second year university students in Hawassa, Ethiopia. The sample comprised of 506 males and 414 females, 400 students from Government schools and 249 from private schools, and 271 from the university. The data were analysed by means of descriptive statistics (means and correlations) and inferential statistics (analysis of variance). The results indicated that the majority of the students’ learning styles were balanced between the two dimensions of the ILS scales. As secondary preference, they tended towards moderate categories, and a small section of the students preferred the strong categories of the scales. Secondly, the study determined that the sampled students in general, demonstrated a positive attitude towards active learning. Thirdly, by means of the study a significant relationship was ascertained between the students’ attitudes towards active learning and the active-reflective dimension of the ILS. Fourthly, significant differences were indicated in the students’ learning styles and attitudes towards active learning in respect of their gender, their education level and the types of schools. / Psychology of Education / D. Ed. (Psychology of Education)

With cancer being one of the leading causes of death globally, and with oral cancers being among the most common types of cancer, it is of interest to conduct large-scale oral cancer screening among the general population. Deep Learning can be used to make this possible despite the medical expertise required for early detection of oral cancers. A bottleneck of Deep Learning is the large amount of data required to train a good model. This project investigates two topics: certainty calibration, which aims to make a machine learning model produce more reliable predictions, and Active Learning, which aims to reduce the amount of data that needs to be labeled for Deep Learning to be effective. In the investigation of certainty calibration, five different methods are compared, and the best method is found to be Dirichlet calibration. The Active Learning investigation studies a single method, Cost-Effective Active Learning, but it is found to produce poor results with the given experiment setting. These two topics inspire the further development of the cytological annotation tool CytoBrowser, which is designed with oral cancer data labeling in mind. The proposedevolution integrates into the existing tool a Deep Learning-assisted annotation workflow that supports multiple users.

This work deals with the creation of a system that allows uploading and annotating scans of historical documents and subsequent active learning of models for character recognition (OCR) on available annotations (marked lines and their transcripts). The work describes the process, classifies the techniques and presents an existing system for character recognition. Above all, emphasis is placed on machine learning methods. Furthermore, the methods of active learning are explained and a method of active learning of available OCR models from annotated scans is proposed. The rest of the work deals with a system design, implementation, available datasets, evaluation of self-created OCR model and testing of the entire system.

Lists of records in machine-printed documents contain much useful information. As one example, the thousands of family history books scanned, OCRed, and placed on-line by FamilySearch.org probably contain hundreds of millions of fact assertions about people, places, family relationships, and life events. Data like this cannot be fully utilized until a person or process locates the data in the document text, extracts it, and structures it with respect to an ontology or database schema. Yet, in the family history industry and other industries, data in lists goes largely unused because no known approach adequately addresses all of the costs, challenges, and requirements of a complete end-to-end solution to this task. The diverse information is costly to extract because many kinds of lists appear even within a single document, differing from each other in both structure and content. The lists' records and component data fields are usually not set apart explicitly from the rest of the text, especially in a corpus of OCRed historical documents. OCR errors and the lack of document structure (e.g. HMTL tags) make list content hard to recognize by a software tool developed without a substantial amount of highly specialized, hand-coded knowledge or machine learning supervision. Making an approach that is not only accurate but also sufficiently scalable in terms of time and space complexity to process a large corpus efficiently is especially challenging. In this dissertation, we introduce a novel family of scalable approaches to list discovery and ontology population. Its contributions include the following. We introduce the first general-purpose methods of which we are aware for both list detection and wrapper induction for lists in OCRed or other plain text. We formally outline a mapping between in-line labeled text and populated ontologies, effectively reducing the ontology population problem to a sequence labeling problem, opening the door to applying sequence labelers and other common text tools to the goal of populating a richly structured ontology from text. We provide a novel admissible heuristic for inducing regular expression wrappers using an A* search. We introduce two ways of modeling list-structured text with a hidden Markov model. We present two query strategies for active learning in a list-wrapper induction setting. Our primary contributions are two complete and scalable wrapper-induction-based solutions to the end-to-end challenge of finding lists, extracting data, and populating an ontology. The first has linear time and space complexity and extracts highly accurate information at a low cost in terms of user involvement. The second has time and space complexity that are linear in the size of the input text and quadratic in the length of an output record and achieves higher F1-measures for extracted information as a function of supervision cost. We measure the performance of each of these approaches and show that they perform better than strong baselines, including variations of our own approaches and a conditional random field-based approach.

Machine Learning models trained using supervised learning can achieve great results when a sufficient amount of labeled data is used. However, the annotation process is a costly and time-consuming task. There are many methods devised to make the annotation pipeline more user and data efficient. This thesis explores techniques from Active Learning, Zero-shot Learning, Data Augmentation domains as well as pre-annotation with revision in the context of multi-label classification. Active ’Learnings goal is to choose the most informative samples for labeling. As an Active Learning state-of-the-art technique Contrastive Active Learning was adapted to a multi-label case. Once there is some labeled data, we can augment samples to make the dataset more diverse. English-German-English Backtranslation was used to perform Data Augmentation. Zero-shot learning is a setup in which a Machine Learning model can make predictions for classes it was not trained to predict. Zero-shot via Textual Entailment was leveraged in this study and its usefulness for pre-annotation with revision was reported. The results on the Reviews of Electric Vehicle Charging Stations dataset show that it may be beneficial to use Active Learning and Data Augmentation in the annotation pipeline. Active Learning methods such as Contrastive Active Learning can identify samples belonging to the rarest classes while Data Augmentation via Backtranslation can improve performance especially when little training data is available. The results for Zero-shot Learning via Textual Entailment experiments show that this technique is not suitable for the production environment. / Klassificeringsmodeller som tränas med övervakad inlärning kan uppnå goda resultat när en tillräcklig mängd annoterad data används. Annoteringsprocessen är dock en kostsam och tidskrävande uppgift. Det finns många metoder utarbetade för att göra annoteringspipelinen mer användar- och dataeffektiv. Detta examensarbete utforskar tekniker från områdena Active Learning, Zero-shot Learning, Data Augmentation, samt pre-annotering, där annoterarens roll är att verifiera eller revidera en klass föreslagen av systemet. Målet med Active Learning är att välja de mest informativa datapunkterna för annotering. Contrastive Active Learning utökades till fallet där en datapunkt kan tillhöra flera klasser. Om det redan finns några annoterade data kan vi utöka datamängden med artificiella datapunkter, med syfte att göra datasetet mer mångsidigt. Engelsk-Tysk-Engelsk översättning användes för att konstruera sådana artificiella datapunkter. Zero-shot-inlärning är en teknik i vilken en maskininlärningsmodell kan göra förutsägelser för klasser som den inte var tränad att förutsäga. Zero-shot via Textual Entailment utnyttjades i denna studie för att utöka datamängden med artificiella datapunkter. Resultat från datamängden “Reviews of Electric Vehicle Charging ”Stations visar att det kan vara fördelaktigt att använda Active Learning och Data Augmentation i annoteringspipelinen. Active Learning-metoder som Contrastive Active Learning kan identifiera datapunkter som tillhör de mest sällsynta klasserna, medan Data Augmentation via Backtranslation kan förbättra klassificerarens prestanda, särskilt när få träningsdata finns tillgänglig. Resultaten för Zero-shot Learning visar att denna teknik inte är lämplig för en produktionsmiljö.

D. Tech. Education / The study reports on a teaching strategy for active learning using clicker mobile technology with mathematics students. The study focuses on the large class groups, poor class attendance and lack of student participation. It also focuses on lack of immediate feedback on student learning throughout the lesson and the insufficient time for regular formative assessment.

Technology is enjoying an increasingly important role in many collegiate pedagogical designs. Contemporary research has become more focused on the ways that technology can contribute to learning outcomes. These studies provide a critical foundation for educational researchers who seek to incorporate and reap the benefits of new technologies in classroom environments. The aim of the present study is to empirically assess how combining an active, collaborative learning environment with a classroom response system (colloquially called “clickers”) in a postsecondary setting can influence and improve learning outcomes. To this end, the study proposes an instructional design utilizing two feedback response-formats (clickers and flashcards) and two response methods for answering in-class questions (collaborative peer instruction and individual). The theoretical bases that provide the academic structure for the five instructional conditions (control, clicker-response individual, clicker-response peer instruction, flashcard-response individual, and flashcard-response peer instruction) are the generative learning theory and social constructivism. Participants were 171 undergraduate students from an Educational Psychology subject pool from a large Southwest university. The researcher used a two-way analysis of covariance (ANCOVA) with two treatments (response format and collaboration level) as the between-subjects factors; students’ posttest scores as the dependent variable; and pretest scores as the covariate. Results showed no significant main effects; however, the study produced statistically significant findings that there was an interaction effect between the use of clickers and a peer instruction design. To follow up the interaction, the researcher conducted tests of the simple effects of response format within each collaboration condition, with the pretest as the covariate. Results showed that for students who collaborated, clickers were better than flashcards, whereas when students worked individually, there was no difference. This study builds upon existing studies by using a stronger empirical approach with more robust controls to evaluate the effects of a variety of instructional interventions, clicker and flashcard response systems and peer instruction on learning outcomes. It shows that clicker technology might be most effective when combined with collaborative methods. The discussion includes implications, limitations, and directions for future research. / text

Teisės ir pareigos yra neatskiriama kiekvieno visuomenės nario gyvenimo sociume dalis. Aukštojo mokslo institucijos užima reikšmingą vietą, formuojant asmenų teisinį suvokimą, todėl teisinis ugdymas ir jo dėstymas yra aktuali tema. Aktyvaus mokymo metodai yra plačiai tirti ikimokyklinio ir bendrojo ugdymo lygmenyse. Tačiau aktyviųjų mokymo metodų naudojimas aukštojo mokslo institucijose yra mažai tirtas. Ypatingas dėmesys turėtų būti skiriamas studijų organizavimui, metodų naudojimui teisinio ugdymo paskaitose. Aktyviųjų mokymo metodų naudojimas yra būdas pagerinti teisinio ugdymo dėstymą aukštojoje mokykloje. Darbo tema – aktyviųjų mokymo metodų taikymas teisinio ugdymo paskaitose. Darbo objektas yra aktyviųjų mokymo metodų taikymas. Todėl iškeliamas darbo tikslas - nustatyti aktyviųjų mokymo metodų taikymo poveikį teisinio ugdymo paskaitoms. Atsižvelgiant į darbo tikslą, formuluojama hipotezė - aktyviųjų mokymo metodų naudojimas teisinio ugdymo paskaitose formuoja studentų kritinį mąstymą. Siekiant užsibrėžto tikslo, iškeliami tokie uždaviniai: Apibūdinti aktyviuosius mokymo metodus; išryškinti teisinio ugdymo ypatumus; Ištirti aktyviųjų mokymo metodų panaudojimą teisinio ugdymo paskaitose. Darbo teorinė dalis susideda iš teisinio ugdymo sampratos analizės bei aktyviųjų mokymo metodų aptarimo. Teisinis ugdymas nagrinėjamas aiškinant teisės ir ugdymo sąvokas. Antroje darbo dalyje yra aptariama aktyviųjų mokymo metodų samprata, įvairovė bei jų taikymo aukštojoje mokymo... [toliau žr. visą tekstą] / Rights and duties have a huge impact on everyday life of each member of our society. Higher education institutions play an important role in forming the understanding of law. Therefore the teaching of law in higher education institutions is an urgent issue of nowadays life. Active teaching methods are widely researched in kindergarten and school environment. Usage of active teaching strategies in higher education is less explored; therefore it should be examined for the improvement of such programs as law education. The subject of this work is the implementation of active teaching methods in higher education law studies. The objective of the work is to find out the impact which active methods have on law education lectures. According to the aim, the hypothesis is pointed out – the usage of active methods in law education lectures forms critical thinking. Therefore, according to the objective mentioned before, three main goals are pointed out: description of active teaching methods; emphasis of law education features; research of active teaching methods usage in higher education law studies. The first part of the work is composed of law education research through the law and the education concept analysis. The second part of the written work consists of the active method concept, variety and application possibility in higher education research. The last part describes the survey, which is constructed for the approval or rejection of the hypothesis and for the achievement of... [to full text]

Les méthodes d’apprentissage évoluent et aux modèles classiques d’enseignement viennent s’ajouter de nouveaux paradigmes, dont les systèmes d’information et de communication, notamment le Web, sont une partie essentielle. Afin améliorer la capacité de traitement de l’information de ces systèmes, le Web sémantique définit un modèle de description de ressources (Resource Description Framework – RDF), ainsi qu’un langage pour la définition d’ontologies (Web Ontology Language – OWL). Partant des concepts, des méthodes, des théories d’apprentissage, en suivant une approche systémique, nous avons utilisé les technologies du Web sémantique pour réaliser une plateforme d’apprentissage capable d’enrichir et de personnaliser l’expérience de l’apprenant. Les résultats de nos travaux sont concrétisés dans la proposition d’un prototype pour un Système Actif et Sémantique d’Apprentissage (SASA). Suite à l’identification et la modélisation des entités participant à l’apprentissage, nous avons construit six ontologies, englobant les caractéristiques de ces entités. Elles sont les suivantes : (1) ontologie de l’apprenant, (2) ontologie de l’objet pédagogique, (3) ontologie de l’objectif d’apprentissage, (4) ontologie de l’objet d’évaluation, (5) ontologie de l’objet d’annotation et (6) ontologie du cadre d’enseignement. L’intégration des règles au niveau des ontologies déclarées, cumulée avec les capacités de raisonnement des moteurs d’inférences incorporés au niveau du noyau sémantique du SASA, permettent l’adaptation du contenu d’apprentissage aux particularités des apprenants. L’utilisation des technologies sémantiques facilite l’identification des ressources d’apprentissage existant sur le Web ainsi que l’interprétation et l’agrégation de ces ressources dans le cadre du SASA / Learning methods keep evolving and new paradigms are added to traditional teaching models where the information and communication systems, particularly the Web, are an essential part. In order to improve the processing capacity of information systems, the Semantic Web defines a model for describing resources (Resource Description Framework - RDF), and a language for defining ontologies (Web Ontology Language – OWL). Based on concepts, methods, learning theories, and following a systemic approach, we have used Semantic Web technologies in order to provide a learning system that is able to enrich and personalize the experience of the learner. As a result of our work we are proposing a prototype for an Active Semantic Learning System (SASA). Following the identification and modeling of entities involved in the learning process, we created the following six ontologies that summarize the characteristics of these entities: (1) learner ontology, (2) learning object ontology, (3) learning objective ontology, (4) evaluation object ontology, (5) annotation object ontology and (6) learning framework ontology. Integrating certain rules in the declared ontologies combined with reasoning capacities of the inference engines embedded in the kernel of the SASA, allow the adaptation of learning content to the characteristics of learners. The use of semantic technologies facilitates the identification of existing learning resources on the web as well as the interpretation and aggregation of these resources within the context of SASA