11 research outputs found
Recommending learning material in Intelligent Tutoring Systems
Nowadays, intelligent e-learning systems which can adapt to learner's needs and preferences, became very popular. Many studies have demonstrated that such systems can increase the eects of learning. However, providing adaptability requires consideration of many factors. The main problems concern user modeling and personalization, collaborative learning, determining and modifying learning senarios, analyzing learner's learning styles. Determining the optimal learning scenario adapted to students' needs is very important part of an e-learning system. According to psychological research, learning path should follow the students' needs, such as (i.a.): content, level of diculty or presentation version. Optimal learning path can allow for easier and faster gaining of knowledge. In this paper an overview of methods for recommending learning material is presented. Moreover, a method for determining a learning scenario in Intelligent Tutoring Systems is proposed. For this purpose, an Analytic Hierarchy Process (AHP) method is used
E3: Emotions, Engagement, and Educational Digital Games
The use of educational digital games as a method of instruction for science, technology, engineering, and mathematics has increased in the past decade. While these games provide successfully implemented interactive and fun interfaces, they are not designed to respond or remedy studentsâ negative affect towards the game dynamics or their educational content. Therefore, this exploratory study investigated the frequent patterns of student emotional and behavioral response to educational digital games.
To unveil the sequential occurrence of these affective states, students were assigned to play the game for nine class sessions. During these sessions, their affective and behavioral response was recorded to uncover possible underlying patterns of affect (particularly confusion, frustration, and boredom) and behavior (disengagement). In addition, these affect and behavior frequency pattern data were combined with studentsâ gameplay data in order to identify patterns of emotions that led to a better performance in the game.
The results provide information on possible affect and behavior patterns that could be used in further research on affect and behavior detection in such open-ended digital game environments. Particularly, the findings show that students experience a considerable amount of confusion, frustration, and boredom. Another finding highlights the need for remediation via embedded help, as the students referred to peer help often during their gameplay. However, possibly because of the low quality of the received help, students seemed to become frustrated or disengaged with the environment. Finally, the findings suggest the importance of the decay rate of confusion; studentsâ gameplay performance was associated with the length of time students remained confused or frustrated. Overall, these findings show that there are interesting patterns related to students who experience relatively negative emotions during their gameplay
Proceedings of the ECMLPKDD 2015 Doctoral Consortium
ECMLPKDD 2015 Doctoral Consortium was organized for the second time as part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), organised in Porto during September 7-11, 2015. The objective of the doctoral consortium is to provide an environment for students to exchange their ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in machine learning, data mining, and related areas. These proceedings collect together and document all the contributions of the ECMLPKDD 2015 Doctoral Consortium
5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)
Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 5th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.MartĂnez Torres, MDR.; Toral MarĂn, S. (2023). 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2023.2023.1700
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Hypernetworks Analysis of RoboCup Interactions
Robotic soccer simulations are controlled environments in which the rich variety of interactions among agents make them good candidates to be studied as complex adaptive systems. The challenge is to create an autonomous team of soccer agents that can adapt and improve its behaviour as it plays other teams. By analogy with chess, the movements of the soccer agents and the ball form ever-changing networks as players in one team form structures that give their team an advantage. For example, the Defenderâs Dilemma involves relationships between an attacker with the ball, a team-mate and a defender. The defender must choose between tackling the player with the ball, or taking a position to intercept a pass to the other attacker. Since these structures involve more that two interacting entities it is necessary to go beyond networks to multidimensional hypernetworks. In this context, this thesis investigates (i) is it possible to identify patterns of play, that lead a team to obtain an advantage ?, (ii) is it possible to forecast with a good degree of accuracy if a certain game action or sequence of game actions is going to be successful, before it has been completed ?, and (iii) is it possible to make behavioural patterns emerge in the game without specifying the behavioural rules in detail ? To investigate these research questions we devised two methods to analyse the interactions between robotic players, one based on traditional programming and one based on Deep Learning. The first method identified thousands of Defenderâs Dilemma configurations from RoboCup 2D simulator games and found a statistically significant association between winning and the creation of the defenderâs dilemma by the attackers of the winning team. The second method showed that a feedforward Artificial Neural Network trained on thousands of games can take as input the current game configuration and forecast to a high degree of accuracy if the current action will end up in a goal or not. Finally, we designed our own fast and simple robotic soccer simulator for investigating Reinforcement Learning. This showed that Reinforcement Learning using Proximal Policy Optimization could train two agents in the task of scoring a goal, using only basic actions without using pre-built hand-programmed skills. These experiments provide evidence that it is possible: to identify advantageous patterns of play; to forecast if an action or sequence of actions will be successful; and to make behavioural patterns emerge in the game without specifying the behavioural rules in detail
Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System
This thesis presents research that combines the benefits of intelligent tutoring
systems (ITS), conversational agents (CA) and learning styles theory by constructing
a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS
aims to imitate a human tutor by implicitly predicting individualsâ learning style
preferences and adapting its tutoring style to suit them during a tutoring
conversation.
ITS are computerised learning systems that intelligently personalise tutoring
based on learner characteristics such as existing knowledge and learning style. ITS
are traditionally student-led, hyperlink-based learning systems that adapt the
presentation of learning resources by reordering or hiding links. Research suggests
that students learn more effectively when instruction matches their learning style,
which is typically modelled explicitly using questionnaires or implicitly based on
behaviour. Learning is a social process and natural language interfaces to ITS, such
as CAs, allow students to construct knowledge through discussion. Existing CITS
adapt tutoring according to student knowledge, emotions and mood, however no
CITS adapts to learning styles.
Oscar CITS models a human tutor by directing a tutoring conversation and
automatically detecting and adapting to an individualâs learning styles. Original
methodologies and architectures were developed for constructing an Oscar Predictive
CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured
from a learning styles model to dynamically predict learning styles from an
individualâs tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation
algorithm to select the best tutoring style for each tutorial question. The Oscar CITS
methodologies and architectures are independent of the learning styles model and
subject domain. Empirical studies involving real students have validated the
prediction and adaptation of learning styles in a real-world teaching/learning
environment. The results show that learning styles can be successfully predicted
from a natural language tutoring dialogue, and that adapting the tutoring style
significantly improves learning performance