Measuring the difficulty of activities for adaptive learning

An effective adaptive learning system would theoretically maintain learners in a permanent state of flow. In this state, learners are completely focused on activities. To attain this state, the difficulty of learning activities must match learners’ skills. To perform this matching, it is essential to define, measure and deeply analyze difficulty. However, very few previous works deal with difficulty in depth. Most commonly, difficulty is defined as a one-dimensional value. This permits ordering activities, but limits the possibilities of deep analysis of activities and learners’ performance. This work proposes a new definition of difficulty and a way to measure it. The proposed definition depends on learners’ progress on activities over time. This expands the concept of difficulty over a two-dimensional space, also making it drawable. The difficulty graphs provide a rich interpretation with insights into the learning process. A practical case is presented: the PLMan learning system. This system is formed by a web application and a game to teach computational logic. The proposed definition is applied in this context. Measures are taken and analyzed using difficulty graphs. Some examples of these analyses are shown to illustrate the benefits of this proposal. Singularities and interesting spots are easily identified in graphs, providing insights in the activities. This new information lets experts adapt the learning system by improving activity classification and assignment. This first step lays solid foundations for automation, making the PLMan learning system fully adaptive

Measuring the difficulty of activities for adaptive learning

An effective adaptive learning system would theoretically maintain learners in a permanent state of flow. In this state, learners are completely focused on activities. To attain this state, the difficulty of learning activities must match learners’ skills. To perform this matching, it is essential to define, measure and deeply analyze difficulty. However, very few previous works deal with difficulty in depth. Most commonly, difficulty is defined as a one-dimensional value. This permits ordering activities, but limits the possibilities of deep analysis of activities and learners’ performance. This work proposes a new definition of difficulty and a way to measure it. The proposed definition depends on learners’ progress on activities over time. This expands the concept of difficulty over a two-dimensional space, also making it drawable. The difficulty graphs provide a rich interpretation with insights into the learning process. A practical case is presented: the PLMan learning system. This system is formed by a web application and a game to teach computational logic. The proposed definition is applied in this context. Measures are taken and analyzed using difficulty graphs. Some examples of these analyses are shown to illustrate the benefits of this proposal. Singularities and interesting spots are easily identified in graphs, providing insights in the activities. This new information lets experts adapt the learning system by improving activity classification and assignment. This first step lays solid foundations for automation, making the PLMan learning system fully adaptive

Teaching through Learning Analytics: Predicting Student Learning Profiles in a Physics Course at a Higher Education Institution

Learning Analytics (LA) is increasingly used in Education to set prediction models from artificial intelligence to determine learning profiles. This study aims to determine to what extent K-nearest neighbor and random forest algorithms could become a useful tool for improving the teaching-learning process and reducing academic failure in two Physics courses at the Technological Institute of Monterrey, México (n = 268). A quasi-experimental and mixed method approach was conducted. The main results showed significant differences between the first and second term evaluations in the two groups. One of the main findings of the study is that the predictions were not very accurate for each student in the first term evaluation. However, the predictions became more accurate as the algorithm was fed with larger datasets from the second term evaluation. This result indicates how predictive algorithms based on decision trees, can offer a close approximation to the academic performance that will occur in the class, and this information could be use along with the personal impressions coming from the teacher

¿Cómo pueden responder las universidades a los retos derivados de la transformación digital? Modelos educativos flexibles

La ponencia pretende ser el punto de partida del debate sobre si la universidad, a través de sus modelos actuales de formación, puede dar respuesta a los retos que le demanda la sociedad digital. Para empezar, haré un poco de historia, tomaré impulso apoyándome en algunos conceptos básicos, para finalmente poner el foco en la transformación digital y en la educación. El profesor puede generar un entorno en el que se favorezca el aprendizaje, pero en última instancia debe ser el propio aprendiz el que debe asumir un papel activo en su aprendizaje. El paradigma actual de educación, la docencia de talla única, herencia de la era industrial y la fabricación en serie, no es válida para el mundo digital. Y aunque la transformación digital es más que una simple inyección de tecnología, comprender cómo se comportan estas nuevas tecnologías nos va a ayudar en nuestro empeño. Además, las universidades no partimos de cero, arrastramos una larga historia, disponemos de personal y espacios ya operativos. Tenemos, por tanto, nuestros vicios y nuestras virtudes. Y cualquier cambio (evolución y transformación) debe tener esto en cuenta. Entrando en el núcleo de la charla, tras soñar cómo me gustaría que fuese la plataforma de aprendizaje, haré el planteamiento del proyecto en el que estamos trabajando, Smart System based on Adaptive Learning Itineraries. El nuevo paradigma formativo para la sociedad del conocimiento debe ser diseñado para maximizar el aprendizaje, hacer un tratamiento personalizado, estar basado en tareas, midiendo continuamente los logros obtenidos, con una evaluación formativa y promoviendo la motivación intrínseca. La nueva revolución de la tecnología educativa vendrá de aplicaciones que reconozcan las necesidades de aprendizaje del usuario y que adaptan su avance a un ritmo personalizado, lo que llamo metafóricamente docencia líquida. Necesitamos un diseño del proceso docente y unas plataformas recolectoras de información que alimenten los sistemas de análisis de datos; y la aplicación de técnicas de inteligencia artificial al análisis de estos datos permitirá adaptar las actividades docentes a las particularidades y al ritmo de cada aprendiz. Finalmente, antes de dar paso al debate, dejaré planteados los aspectos en los que en mi opinión las universidades necesitan de mayor flexibilidad para adaptarse a la rápida evolución de los tiempos actuales: contenidos de los planes de estudio, titulaciones y perfiles profesionales; interacción con los estudiantes; preparación, tanto pedagógica como tecnológica, de sus profesores; plataformas diseñadas desde el punto de vista del usuario; y normativas, sencillas, claras y breves, que doten de flexibilidad a todo el sistema. La gran distancia existente entre las tecnologías emergentes y las metodologías docentes provoca que los nuevos avances tecnológicos no tengan fácil su integración en los contextos y prácticas metodológicas implantados en nuestras universidades. Y que las tecnologías educativas maduras y los métodos educativos aplicados no respondan a las demandas de la sociedad ni al potencial transformador de la tecnología para la mejora del aprendizaje. ¿Seremos capaces de cerrar esta brecha

Adaptive Learning Technology in Primary Education: Implications for Professional Teacher Knowledge and Classroom Management

The aim of this study is to explore the introduction of Adaptive Learning Technology (ALT) and inherent Learning Analytics (LA) in the classroom management and professionalism of teachers in a primary education real-life context. ALT is characterized by an inherent opportunity to personalize curriculum and learning experiences for each individual learner and to support teacher-facilitated learning. In this mixed methods study, we explore upper-primary teachers understanding of ALT application in real-life context, and we take a closer look at their experiences with ALT in their own context and practice through three different methodological lenses. The study offers insight into how teachers think and reason as they integrate ALT in their practice and addresses advantages and disadvantages of using ALT technology in primary education learning ecologies. The study also aims to discuss some more general implications of applying ALT and LA in primary and secondary level learning ecologies and concludes that automated system affordances and constraints can create new challenges for teachers, which exceeds teachers’ digital competence and ability to make use of certain real and perceived affordances.publishedVersio

APLICACIÓN DEL JUEGO SERIO EN PROGRAMAS DE CIENCIAS ECONÓMICAS: TENDENCIAS Y DESAFÍOS

Este documento presenta una caracterización del trabajo colaborativo internacional sobre la aplicación del juego serio en contenidos curriculares de programas en ciencias económicas relacionadas con las finanzas, la contabilidad y la administración. Se tomó como referencia información compilada de la base de datos académica Scopus durante la ventana de observación 2007-2018, teniendo en cuenta indicadores bibliométricos, metodologías destacadas, tendencias de uso o aplicación y desafíos o retos de la implementación. Como resultado, se logra identificar ocho principales categorías de estrategias de enseñanza-aprendizaje en las cuales Aprendizaje adaptativo, Aprendizaje colaborativo y Gamificación se ubican como las tendencias en las que el uso del juego serio se ha consolidado. Storytelling (narrativa de historias), y el Aprendizaje basado en competencias están en proceso de consolidación. Finalmente, Aprendizaje invertido, Massive Open Online Course (MOOC), y Aprendizaje basado en retos son líneas incipientes en las que el uso del juego serio no se ha desarrollado lo suficiente. De igual manera, se logra destacar, a partir del seguimiento de publicaciones científicas, los principales logros, orientaciones y desafíos de la aplicación de estas estrategias en el aula de clase; particularmente, las relacionadas con las finanzas, la administración y la contaduría. 

Designing for “challenge” in a large‐scale adaptive literacy game for primary school children

The use of learning games within the classroom is becoming increasingly common because of their potential to positively impact learning. Recent developments in adaptivity offer further possibilities to personalise learning by tailoring the game to an individual child's level or particular learning needs. However, designing an adaptive learning game is a complex process as many different game components have an impact on the provision of optimal challenge, crucial for maintaining player engagement, with limited prior work considering the multifaceted nature of this concept. This paper explores how to design for “challenge” within large-scale adaptive learning games through a case study focused on the design of a literacy game for three linguistically and cognitively diverse learner groups—novice readers, children with dyslexia and children learning English as a foreign language. In reflecting on our design process, we identify three key design tensions that arose: (a) supporting longer-term learning goals through game replayability; (b) fostering either replication or innovation in pedagogy through adaptivity rules; and (c) addressing diversity between learner groups. We present a set of design recommendations to guide researchers and designers in taking a multidimensional view of challenge when designing large-scale adaptive learning games

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

Cognitive Decay And Memory Recall During Long Duration Spaceflight

This dissertation aims to advance the efficacy of Long-Duration Space Flight (LDSF) pre-flight and in-flight training programs, acknowledging existing knowledge gaps in NASA\u27s methodologies. The research\u27s objective is to optimize the cognitive workload of LDSF crew members, enhance their neurocognitive functionality, and provide more meaningful work experiences, particularly for Mars missions.The study addresses identified shortcomings in current training and learning strategies and simulation-based training systems, focusing on areas requiring quantitative measures for astronaut proficiency and training effectiveness assessment. The project centers on understanding cognitive decay and memory loss under LDSF-related stressors, seeking to establish when such cognitive decline exceeds acceptable performance levels throughout mission phases. The research acknowledges the limitations of creating a near-orbit environment due to resource constraints and the need to develop engaging tasks for test subjects. Nevertheless, it underscores the potential impact on future space mission training and other high-risk professions. The study further explores astronaut training complexities, the challenges encountered in LDSF missions, and the cognitive processes involved in such demanding environments. The research employs various cognitive and memory testing events, integrating neuroimaging techniques to understand cognition\u27s neural mechanisms and memory. It also explores Rasmussen\u27s S-R-K behaviors and Brain Network Theory’s (BNT) potential for measuring forgetting, cognition, and predicting training needs. The multidisciplinary approach of the study reinforces the importance of integrating insights from cognitive psychology, behavior analysis, and brain connectivity research. Research experiments were conducted at the University of North Dakota\u27s Integrated Lunar Mars Analog Habitat (ILMAH), gathering data from selected subjects via cognitive neuroscience tools and Electroencephalography (EEG) recordings to evaluate neurocognitive performance. The data analysis aimed to assess brain network activations during mentally demanding activities and compare EEG power spectra across various frequencies, latencies, and scalp locations. Despite facing certain challenges, including inadequacies of the current adapter boards leading to analysis failure, the study provides crucial lessons for future research endeavors. It highlights the need for swift adaptation, continual process refinement, and innovative solutions, like the redesign of adapter boards for high radio frequency noise environments, for the collection of high-quality EEG data. In conclusion, while the research did not reveal statistically significant differences between the experimental and control groups, it furnished valuable insights and underscored the need to optimize astronaut performance, well-being, and mission success. The study contributes to the ongoing evolution of training methodologies, with implications for future space exploration endeavors

أثر إستراتيجية تحليل المهمة القائمة على البيداغوجيا الفارقية في رفع مستوى تحصيل ذوي صعوبات تعلم الرياضيات

هدفت الدراسة الحالية قياس أثر إستراتيجية تحليل المهمة القائمة على البيداغوجية الفارقية في رفع مستوى تحصيل ذوي صعوبات تعلم الرياضيات، طبقت الدراسة على عينة قوامها (40) تلميذا وتلميذة من تلاميذ السنة الخامسة ابتدائي، قسمت بطريقة عشوائية إلى مجموعتين ضابطة وتجريبية قوام كل منهما (20) تلميذا وتلميذة. ولمعالجة المشكلة المطروحة استخدمت الباحث المنهج التجريبي، ولتحقيق أهداف الدراسة اعتمد أدوات الدراسة المتمثلة في مقياس التقدير التشخيصي لصعوبات تعلم الرياضيات ومقياس التقدير التشخيصي لصعوبات السلوك الاجتماعي والانفعالي إعداد (الزيات، 2007) تقنين وتكييف (عمراني، 2016)، اختبار المصفوفات المتتابعة الملونة لرافن للأطفال والكبار لـ: جون رافن تقنيين (قدي،2017)، استمارة المستوى الاقتصادي والاجتماعي للأسرة إعداد (بن فليس، 2009)، اختبار تحصيلي في الرياضيات (أعداد الباحث) واستراتيجية تحليل المهمة قائمة على البيداغوجيا الفارقية (إعداد الباحث). ولتحليل وتفسير النتائج استخدم الباحث الأساليب الإحصائية الآتية: النسب المئوية. المتوسط الحسابي، الانحراف المعياري، معامل ألفا كرونباخ، معامل الارتباط بيرسون، عامل سبيرمان براون لحساب الثبات بطريقة التجزئة النصفية، اختبار كولموقروف سميرنوف واختبار شابيروويلك لحساب اعتدالية التوزيع، اختبار ليفين للتحقق من التجانس، معامل إيتا مربع لقياس حجم الأثر، معامل بلانك للكسب المعدل، اختبار (ت) وتحليل التباين الأحادي. باستخدام برنامج الحزمة الإحصائية للعلوم الاجتماعية الإصدار 22 وبرنامج ميكروسوفت أفيس إكسل الإصدار 21 وأسفرت الدراسة على النتائج الآتية: • ترفع استراتيجية تحليل المهمة القائمة على البيداغوجيا الفارقية من مستوى تحصيل ذوي صعوبات تعلم الرياضيات. • لا توجد فروق ذات دلالة إحصائية في تحصيل التلاميذ ذوي صعوبات تعلم الرياضيات بين متوسطي درجات المجموعة التجريبية ومتوسطي درجات المجموعة الضابطة في القياس القبلي. • توجد فروق ذات دلالة إحصائية بين متوسطي درجات المجموعة التجريبية والمجموعة الضابطة في تحصيل التلاميذ ذوي صعوبات تعلم الرياضيات في الاختبار البعدي. • لا توجد فروق ذات دلالة إحصائية بين متوسطات درجات المجموعة التجريبية في الاختبار البعدي لمادة الرياضيات تعزى لمتغير الجنس. • توجد فروق ذات دلالة إحصائية بين متوسطات درجات المجموعة التجريبية في الاختبار البعدي في مادة الرياضيات تعزى لمتغير سن الالتحاق بالمدرسة (قبل سن التمدرس/ السن القانوني للتمدرس). • توجد فروق ذات دلالة إحصائية في أداء التلاميذ ذوي صعوبات تعلم الرياضيات بين ميادين مادة الرياضيات الأربعة (الأعداد والحساب، تنظيم المعطيات، الفضاء والهندسة، المقادير والقياس) في الاختبار البعدي. • لا توجد فروق ذات دلالة إحصائية بين متوسطات درجات تلاميذ المجموعة التجريبية في القياسين البعدي والتتبعي. The present study aims to measure the impact of the task analysis strategy, based on differential pedagogy, in raising the achievement level of pupils with learning difficulties in mathematics. The study was conducted on a sample of (40) male and female of fifth year primary school pupils. The sample is randomly divided into two groups, control and experimental groups, each consisting of (20) pupils. To address the problem, the researcher opted for the experimental approach, and to achieve the objectives of the study, the following study tools are adopted: the Diagnostic Assessment Scale for Mathematics Learning Difficulties and the Diagnostic Assessment Scale for Social and Emotional Behavioral Difficulties (Al-Zayyat, 2007), Rationing and Conditioning (Omrani, 2016), Raven's Progressive Coloured Matrices Test for Children and Higher Technicians (Qaddy, 2017), Family Economic and Social Level Questionnaire (Ben Fleis, 2009), Mathematics Achievement Test and Task Analysis Strategy based on Differencial Pedagogy (prepared by the researcher). To analyse and interpret the results, the researcher used the following statistical methods: percentages, arithmetic mean, standard deviation, Cronbach's alpha, Pearson's correlation coefficient, Spearman-Brown factor for calculating stability by split-half method, Kolmogoroff-Smirnov and Shapiro -Wilk's test for calculating normality of distribution, Levine's test for testing homogeneity, Eta square coefficient for measuring effect size, Planck's coefficient for adjusted gain, t-test, and one-way analysis of variance, using the Statistical Package for the Social Sciences (SPSS) version 22 and Microsoft Office Excel version 21.The study eventually produced the following results: -The task analysis strategy based on differential pedagogy raises the achievement level of students with learning difficulties in mathematics. - There are no statistically significant differences in the achievement of students with learning difficulties in mathematics between the mean scores of the experimental group and the mean scores of the control group at the pre-measurement. - There are statistically significant differences in the achievement of students with learning difficulties in mathematics between the mean scores of the experimental group and the mean scores of the control group at post-test. There are no statistically significant differences between the experimental group's mean post-test scores in mathematics due to the gender variable. - There are statistically significant differences between the experimental group's mean scores on the mathematics post-test due to the school age variable (pre-school age/legal school age. - There are statistically significant differences in the performance of students with learning difficulties in mathematics across the four domains of mathematics (number and arithmetic, data organization, space and geometry, quantity and measurement) at post-test. - There were no statistically significant differences between the mean scores of students in the experimental group in the post and follow-up measures