Evaluation of topic-based adaptation and student modeling in QuizGuide

This paper presents an in-depth analysis of a nonconventional topic-based personalization approach for adaptive educational systems (AES) that we have explored for a number of years in the context of university programming courses. With this approach both student modeling and adaptation are based on coarse-grained knowledge units that we called topics. Our motivation for the topic-based personalization was to enhance AES transparency for both teachers and students by utilizing typical topic-based course structures as the foundation for designing all aspects of an AES from the domain model to the end-user interface. We illustrate the details of the topic-based personalization technology, with the help of the Web-based educational service QuizGuide—the first system to implement it. QuizGuide applies the topic-based personalization to guide students to the right learning material in the context of an undergraduate C programming course. While having a number of architectural and practical advantages, the suggested coarse-grained personalization approach deviates from the common practices toward knowledge modeling in AES. Therefore, we believe that several aspects of QuizGuide required a detailed evaluation—from modeling accuracy to the effectiveness of adaptation. The paper discusses how this new student modeling approach can be evaluated, and presents our attempts to evaluate it from multiple different prospects. The evaluation of QuizGuide across several consecutive semesters demonstrates that, although topics do not always support precise user modeling, they can provide a basis for successful personalization in AESs

A Course Redesign Project: Adaptive Courseware in Biology

In the summer of 2019, a cooperative team of Biology faculty and a principal investigator worked to develop a solid set of aligned student learning outcomes across the sections of first semester (BIOL 1305) and second semester (BIOL 1306) of introductory Biology.  Additionally, the group worked on course objectives alignment within the scope and sequence of the courses, as well as aligned syllabi. A full course redesign was initiated over the summer, where the goal was to align student learning outcomes (SLOs), assessments, and develop a shared set of syllabi for six sections across two courses of introductory biology.  At UTEP, the overall goal was to integrate adaptive courseware technology tools, open education resources (OER) and active learning strategies within a course redesign in our Learning Management System (LMS), Blackboard, for a number of sections in Biology 1305 and Biology 1306 beginning in the spring of 2020. This is challenging, as much of adaptive courseware technology is not as strong in content as the Biology faculty would like for these classes, although it can help to substantially reduce the costs for students.  The information that follows defines the case study for integrating adaptive courseware within the course redesign process for a series of high enrollment introductory Biology course

Förderung der Lernmotivation durch adaptives E-Learning: komparative Evaluation von Techniken zur adaptiven Nutzerführung

Wenngleich Lernmotivation eine wichtige Einflussgröße eines erfolgreichen Lernprozesses darstellt, ist diese bisher kaum Gegenstand der Forschung im Bereich adaptiver ELearning Systeme. In einem mehrphasigen Studienkonzept soll die Frage beantwortet werden, ob Lernmotivation mithilfe von Adaptation gefördert werden kann. Das aktuelle Papier fokussiert sich daher auf eine komparative Evaluation von drei Adaptationstechniken und deren Einfluss auf die Lernmotivation mit einem Sample von 132 Studierenden in Form einer experimentellen Laboruntersuchung im Vergleich mit einer nicht-adaptiven Version als Kontrollgruppe. Schwerpunkt ist die Auswertung von Logfiles zur Erfassung der Wirksamkeit der Adaptationstechniken und des aktuellen Verlaufs der Lernmotivation während der Arbeit mit einer E-Learning Plattform. Die Adaptation erfolgte auf Basis von Motivations-Selbsteinschätzungen. Die Analyse der Daten gibt erste Rückschlüsse auf die motivationsförderliche Gestaltung der Nutzerführung in E-Learning Systemen

Domain Modeling for Personalized Guidance

This chapter attempts to untangle the relationships between personalized guidance and domain modeling, as well as to explain how domain modeling could be used to provide personalized guidance. The problem of personalized guidance has a long history in the area of adaptive educational systems (AES). In fact, the very first recognized AES SCHOLAR (Carbonell, 1970) focused on guiding students to the most relevant facts and questions about the geography of South America. The SCHOLAR functionality was based on a domain model in the form of a semantic network and an overlay student model. Since that time, a considerable share of research in the field of AES has focused on different kinds of personalized guidance, and the majority of this work relied heavily on domain modeling—which makes these two research directions heavily interconnected

Sistem Pemandu Latihan Individual Adaptif Menggunakan Tautan Anotasi (Link Annotation) dan Pemandu Langsung (Direct Guidance)

Dalam pembelajaran sudah tidak asing lagi istilah latihan bagi siswa. Latihan tersebut ditujukan untuk meningkatkan pengetahuan siswa setelah diberi materi dalam kelas. Pada saat ini, latihan bagi para siswa sebagian besar masih dilakukan dengan metode konvensional, yaitu dengan menyamaratakan setiap soal bagi siswa. Hal ini dianggap tidak terlalu efektif karena sebenarnya setiap siswa memiliki tingkat pengetahuan yang berbeda sehingga penyamarataan pemberian latihan dianggap kurang efisien. Sebelumnya terdapat teknologi dimana semua soal ditampung dalam suatu sistem dan siswa bebas untuk memilih soal mana yang akan dikerjakan. Namun hal ini juga masih mempunyai kekurangan yaitu dari sekian banyak soal yang ada, sistem tidak dapat memberi rekomendasi pada siswa mengenai soal mana yang sebaiknya dikerjakan terlebih dahulu.Pada tugas akhir ini telah dibangun sebuah sistem latihan adaptif dengan tujuan membantu siswa dalam berlatih lebih baik dari latihan konvensional dengan nama Learning Crane. Dengan adaptasi ini, sistem dapat memberi rekomendasi pada siswa mengenai soal mana yang sebaiknya dikerjakan terlebih dahulu berdasarkan tingkat pengetahuan siswa saat ini. Sistem ini mempunyai domain model yang berkaitan dengan topik dan sistem navigasi adaptasi tautan anotasi dan langsung. Pengujian atas sistem dilakukan untuk mengetahui apakah terdapat peningkatan pengetahuan atau tidak. Pengujian melibatkan responden mahasiswa dan kalangan umum. Dari pengujian, didapatkan hasil peningkatan pengetahuan responden dengan rata-rata sebesar 6. Selain itu, dari hasil kuesioner yang diberikan, didapatkan kesimpulan bahwa sistem Learning Crane dengan teknologi adaptasinya membantu siswa lebih baik daripada metode konvensional

Automated labeling of PDF mathematical exercises with word N-grams VSM classification

In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and difficult to scale. It recognizes that automated labeling eases the workload on experts, as seen in previous studies using automatic classification algorithms for research papers and Japanese mathematical exercises. However, these studies didn’t delve into fine-grained labeling. In addition to that, as the use of materials in the system becomes more widespread, paper materials are transformed into PDF formats, which can lead to incomplete extraction. However, there is less emphasis on labeling incomplete mathematical sentences to tackle this problem in the previous research. This study aims to achieve precise automated classification even from incomplete text inputs. To tackle these challenges, we propose a mathematical exercise labeling algorithm that can handle detailed labels, even for incomplete sentences, using word n-grams, compared to the state-of-the-art word embedding method. The results of the experiment show that mono-gram features with Random Forest models achieved the best performance with a macro F-measure of 92.50%, 61.28% for 24-class labeling and 297-class labeling tasks, respectively. The contribution of this research is showing that the proposed method based on traditional simple n-grams has the ability to find context-independent similarities in incomplete sentences and outperforms state-of-the-art word embedding methods in specific tasks like classifying short and incomplete texts

Мodel of electronic education based on semantic adaption of learning objects

Циљ дисертације је развој модела електронског образовања заснованог на семантичкој адаптацији објеката учења. Предложен модел обухвата динамичко праћење напретка и свих активности студента приликом учења како би се одредио тренутни стил учења и извршила персонализација садржаја учења, тј. како би се студенту приказали објекти учења који одговарају утврђеном стилу учења. Технологије семантичког веба и онтологија специјално креирана за сврхе дисертације налазе се у основи овог модела. У циљу тестирања, предложени модел је имплементиран на реалном систему обогаћивањем основне Moodle платформе применом семантичких технологија и развојем додатних модула који омогућавају персонализацију. Резултат примене ових технологија је систем за електронско учење назван MAL. У дисертацији су приказани кључни елементи предложеног модела, функционалности система и начин на који су семантичке технологије имплементиране у MAL систему. Модел предложен у дисертацији је евалуиран да би се упоредили резултати учења након примене неадаптивног приступа и адаптивног приступа заснованог на семантичким технологијама. Истраживање је реализовано на Факултету за предузетнички бизнис, Универзитет Унион Никола Тесла у Београду. Резултати указују да семантичка адаптација садржаја учења имплементирана у MAL систему води ка значајно бољим резултатима учења и побољшаној ефикасности процеса учења у поређењу са неперсонализованим курсевима за електронско учење. Добијени резултати указују на позитиван став студената према развијеном окружењу за електронско учење.The goal of this dissertation is to develop a model of electronic education based on semantic adaptation of learning objects. The proposed model includes real time montoring of student’s progress and activities during learning process in order to detect actual learning styles of the student and enable personalization, i.e. display those learning objects to the student according to detected learning style. Semantic Web technologies and an ontology specially created for the purposes of the dissertation are in the backgrouind of this model. For the purpose of testing, the proposed model was implemented on a real system by enriching the basic Moodle platform using semantic technologies and developing additional modules that enable personalization.The result of applying these technologies is e-learning system named MAL. In this dissertation, the main components of the model, the functionalities of the system and the way semantic technologies have been implemented into the MAL system are presented. The proposed model was evaluated to compare the effectiveness of both non-adaptive approach and adaptive approach based on semantics. The evaluation has been carried out at the Faculty of Entrepreneurial Business, the University Union Nikola Tesla, Belgrade. The results are promising since they indicate that semantic adaptation of learning content implemented in MAL leads to significantly better learning outcome and improved efficiency of the learning process compared to the non-adaptive e-learning courses. They also indicate a positive attitude of students towards the developed learning environment

Adaptive social e-learning for Saudi students: virtual project and group formation recommendation acceptance

With the aid of information and communication technology, e-learning has become the latest model in education. Saudi Arabian universities are currently applying the idea of e-learning to facilitate life-long learning and provide new educational opportunities for students. In particular, e-learning is being strongly supported by the Saudi Ministry of Education. Therefore, the Jusur LMS was created, in order to manage the e-learning process. However, a 'one size fits all' approach, whilst not ideal in general, is especially not appropriate for the Saudi culture. Moreover, there is limited support for students to satisfy their individual needs, especially for implementing collaborative projects. To better understand the Saudi students’ needs, this research focuses on the acceptance of the social personalised e-learning, versus static e-learning and traditional education for Saudi university students, and how the former can cater to Saudi education, instead of offering an identical delivery to all students, regardless of students’ interests, preferences, backgrounds, or knowledge. The results from a relatively large-scale case study at Taibah University point towards Saudi students accepting more easily social personalised e-learning, than static e-learning or classroom education. Additionally, the results revealed that Saudi students cannot be said to perceive usefulness, ease of use, and intention of further use towards the traditional collaborative e-learning system they use (the Jusur system) for group project work. Furthermore, this study analyses the current level of satisfaction and the needs for collaborative team projects, with the aim of predicting further requirements for social personalised e-learning systems. It investigates the needs of the students for best ways for recommending the project, group members and communication tools for the group project, aiming at collecting the requirements for the implementation of the research environment. Additionally, it proposes a framework for recommendation of collaborative project work to function within a social e-Learning System. Additionally, it proposed the architecture of the system. It investigated Saudi Arabian higher education students’ acceptance of a recommended virtual project and recommended group formation for e-learning versus traditional project- and team-formation methods for e-learning. The comparison is based on the well-known technology acceptance model (TAM), the theoretical xi framework which was used for designing the data collection from students. The results of the case study have indicated that a recommended virtual project and recommended group formation for e-learning is more acceptable to Saudi students than current e-learning methods