Fuzzy-based user modelling for motivation assessment in programming learning adaptive web-based education systems

Learning programming is not an easy task and students often find this subject difficult to understand and pass. One way to improve students’ knowledge in programming is by using Intelligent Tutoring System (ITS) through Adaptive Web-Based Education Systems (AWBESs). The objective of ITS is to provide a personalized tutoring that is tailored to the student’s needs. User modelling is one of the key factors that can meet the ITS intended objectives. From the literature, it was discovered that motivation stands out as one of the critical students’ characteristics that need to be considered when designing a user model. However, from the previous studies, it was discovered that almost all the researchers and educators constructed the user model based on knowledge and skills as students’ characteristics. Thus, the aim of this study is to develop a user model based on students’ motivation known as the Motivation Assessment Model. This is a model that is able to assess students’ motivation level and deliver tutorial materials accordingly. The Motivation Assessment Model was developed based on Self-Efficacy theory that contributes to the fundamental motivation factor which influences students’ motivation during the learning process. Furthermore, to assess the motivation level, fuzzy logic technique was applied. A tutoring system was then developed based on the proposed model using ITS architecture and ADDIE instructional design model. In order to determine students’ knowledge level after using the tutoring system, pre- and post-tests were conducted on the controlled group and experimental group (30 and 31 students). The learning achievements between experimental group (mean = 3.00) and control group (mean = 2.00) indicated that the tutoring system is significantly more effective in improving students’ knowledge level compared to the traditional approach. A usability evaluation was also conducted whereby the effectiveness was evaluated at the number of errors (7.5%) and completion rate (86.5%); efficiency (mean = 4.85); satisfaction evaluated at task level (mean = 6.77) and test level (mean = 83.55). As a conclusion, the overall tutoring system usability results are accepted by students in the experimental group. The research contribution to knowledge is the development of the proposed Motivation Assessment Model for ITS

Computer aided learning for entry level accountancy students

Available from British Library Document Supply Centre-DSC:DXN049783 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Pengaruh Aspek Pedagogis dan Interaksi Sistem pada Penilaian Usabilitas Sistem E-Learning

Penelitian ini bertujuan untuk melihat pengaruh faktor pedagogis maupun interaksi sistem pada penilaian usabilitas sistem e-learning. Pendekatan pada penilaian sistem e-learning, yang selama ini terbelah antara bertumpu pada satu faktor, atau berupaya menggabungkan keduanya tanpa membedakan kekhasan karakteristiknya. Upaya unifikasi tanpa membedakan karakter tersebut akan menyulitkan perumusan tindak lanjut hasil penilain yang dilakukan, sebab tindak lanjut pasca penilaian memiliki perbedaan pada kedua jenis faktor tersebut. Faktor-faktor pedagogis maupun usabilitas sistem mungkin berpengaruh, diperoleh dari hasil studi literatur pada penelitian-penelitian sebelumnya. Pengujian terhadap faktor-faktor tersebut dilakukan dengan melakukan survei kepada para pengguna sistem VCLASS di Universitas Indo Global Mandiri (UIGM). Selain diharapkan mendapatkan faktor-faktor yang diperlukan dalam penyusunan kerangka penilaian usabilitas sistem e-learning, penelitian ini juga dapat memberikan kontribusi terhadap penyempurnaan sistem VCLASS UIGM

Modelling e-learner comprehension within a conversational intelligent tutoring system

Conversational Intelligent Tutoring Systems (CITS) are agent based e-learning systems which deliver tutorial content through discussion, asking and answering questions, identifying gaps in knowledge and providing feedback in natural language. Personalisation and adaptation for CITS are current research focuses in the field. Classroom studies have shown that experienced human tutors automatically, through experience, estimate a learner’s level of subject comprehension during interactions and modify lesson content, activities and pedagogy in response. This paper introduces Hendrix 2.0, a novel CITS capable of classifying e-learner comprehension in real-time from webcam images. Hendrix 2.0 integrates a novel image processing and machine learning algorithm, COMPASS, that rapidly detects a broad range of non-verbal behaviours, producing a time-series of comprehension estimates on a scale from -1.0 to +1.0. This paper reports an empirical study of comprehension classification accuracy, during which 51 students at Manchester Metropolitan University undertook conversational tutoring with Hendrix 2.0. The authors evaluate the accuracy of strong comprehension and strong non-comprehension classifications, during conversational questioning. The results show that the COMPASS comprehension classifier achieved normalised classification accuracy of 75%

Developing Learning System in Pesantren The Role of ICT

According to Krashen's affective filter hypothesis, students who are highly motivated have a strong sense of self, enter a learning context with a low level of anxiety, and are much more likely to become successful language acquirers than those who do not. Affective factors, such as motivation, attitude, and anxiety, have a direct impact on foreign language acquisition. Horwitz et al. (1986) mentioned that many language learners feel anxious when learning foreign languages. Thus, this study recruits 100 college students to fill out the Foreign Language Classroom Anxiety Scale (FLCAS) to investigate language learning anxiety. Then, this study designs and develops an affective tutoring system (ATS) to conduct an empirical study. The study aims to improve students’ learning interest by recognizing their emotional states during their learning processes and provide adequate feedback. It is expected to enhance learners' motivation and interest via affective instructional design and then improve their learning performance

A methodology for evaluating intelligent tutoring systems

DissertationThis dissertation proposes a generic methodology for evaluating intelligent tutoring systems (ITSs), and applies it to the evaluation of the SQL-Tutor, an ITS for the database language SQL. An examination of the historical development, theory and architecture of intelligent tutoring systems, as well as the theory, architecture and behaviour of the SQL-Tutor sets the context for this study. The characteristics and criteria for evaluating computer-aided instruction (CAl) systems are considered as a background to an in-depth investigation of the characteristics and criteria appropriate for evaluating ITSs. These criteria are categorised along internal and external dimensions with the internal dimension focusing on the intrinsic features and behavioural aspects of ITSs, and the external dimension focusing on its educational impact. Several issues surrounding the evaluation of ITSs namely, approaches, methods, techniques and principles are examined, and integrated within a framework for assessing the added value of ITS technology for instructional purposes.Educational StudiesM. Sc. (Information Systems

Interactions Between Patterns of Gamer Behaviors and Time-on-Task for Mathematics Remediation in a Game-based HIVE

As the presence of digital game-based learning increases in United States classrooms, understanding their impact on achievement is critical. Digital games for learning offer many potential benefits, including reducing the number of students trapped in a remediation cycle, a contributor to college dropout. Despite the recognized potential of game-based learning, few researchers have explored the relationships between specific patterns of behaviors and types of digital game-based learning environments. The underlying theory for this study was patterns of gamer behaviors may predict in-game behaviors. Archival, third-party data regarding The Lost Function - Episode 1: Sum of the Forgotten Minds by Advanced Training & Learning Technology, LLC was used in this study. Using 4 case groups at the high school and college levels (n=114), self-reported levels of the 3 patterns of gamer behaviors, gender, and age-band were analyzed using multiple regression to determine relationships to time-on-task in a game-based highly interactive virtual environment, designed for mathematics remediation. While the results were inconclusive, this study supported the existing literature regarding gender differences and the lack of mutual exclusivity in behavior typing. Recommendations include additional research in how the statements used in the 3-factor model may be adjusted to allow for a broader population of game players. The social change implication is that further understanding of the relationship between learner traits and digital learning environment may assist educators that employ digital game-based learning a way to better align learners to the most appropriate digital learning environment, thereby increases their chances at success

Feature extraction comparison for facial expression recognition using adaptive extreme learning machine

Facial expression recognition is an important part in the field of affective computing. Automatic analysis of human facial expression is a challenging problem with many applications. Most of the existing automated systems for facial expression analysis attempt to recognize a few prototypes emotional expressions such as anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise. This paper aims to compare feature extraction methods that are used to detect human facial expression. The study compares the gray level co-occurrence matrix, local binary pattern, and facial landmark (FL) with two types of facial expression datasets, namely Japanese female facial expression (JFFE), and extended Cohn-Kanade (CK+). In addition, we also propose an enhancement of extreme learning machine (ELM) method that can adaptively select best number of hidden neurons adaptive ELM (aELM) to reach its maximum performance. The result from this paper is our proposed method can slightly improve the performance of basic ELM method using some feature extractions mentioned before. Our proposed method can obtain maximum mean accuracy score of 88.07% on CK+ dataset, and 83.12% on JFFE dataset with FL feature extraction

A hybrid e-learning framework: Process-based, semantically-enriched and service-oriented

Despite the recent innovations in e-Learning, much development is needed to ensure better learning experience for everyone and bridge the research gap in the current state of the art e-Learning artefacts. Contemporary e-learning artefacts possess various limitations as follows. First, they offer inadequate variations of adaptivity, since their recommendations are limited to e-learning resources, peers or communities. Second, they are often overwhelmed with technology at the expense of proper pedagogy and learning theories underpinning e-learning practices. Third, they do not comprehensively capture the e-learning experiences as their focus shifts to e-learning activities instead of e-learning processes. In reality, learning is a complex process that includes various activities and interactions between different roles to achieve certain gaols in a continuously evolving environment. Fourth, they tend more towards legacy systems and lack the agility and flexibility in their structure and design. To respond to the above limitations, this research aims at investigating the effectiveness of combining three advanced technologies (i.e., Business Process Modelling and Enactment, Semantics and Service Oriented Computing – SOC–) with learning pedagogy in order to enhance the e-learner experience. The key design artefact of this research is the development of the HeLPS e-Learning Framework – Hybrid e-Learning Framework that is Process-based, Semantically-enriched and Service Oriented-enabled. In this framework, a generic e-learning process has been developed bottom-up based on surveying a wide range of e-learning models (i.e., practical artefacts) and their underpinning pedagogies/concepts (i.e., theories); and then forming a generic e-learning process. Furthermore, an e-Learning Meta-Model has been developed in order to capture the semantics of e-learning domain and its processes. Such processes have been formally modelled and dynamically enacted using a service-oriented enabled architecture. This framework has been evaluated using a concern-based evaluation employing both static and dynamic approaches. The HeLPS e-Learning Framework along with its components have been evaluated by applying a data-driven approach and artificially-constructed case study to check its effectiveness in capturing the semantics, enriching e-learning processes and deriving services that can enhance the e-learner experience. Results revealed the effectiveness of combining the above-mentioned technologies in order to enhance the e-learner experience. Also, further research directions have been suggested.This research contributes to enhancing the e-learner experience by making the e-learning artefacts driven by pedagogy and informed by the latest technologies. One major novel contribution of this research is the introduction of a layered architectural framework (i.e., HeLPS) that combines business process modelling and enactment, semantics and SOC together. Another novel contribution is adopting the process-based approach in e-learning domain through: identifying these processes and developing a generic business process model from a set of related e-learning business process models that have the same goals and associated objectives. A third key contribution is the development of the e-Learning Meta-Model, which captures a high-abstract view of learning domain and encapsulates various domain rules using the Semantic Web Rule Language. Additional contribution is promoting the utilisation of Service-Orientation in e-learning through developing a semantically-enriched approach to identify and discover web services from e-learning business process models. Fifth, e-Learner Experience Model (eLEM) and e-Learning Capability Maturity Model (eLCMM) have been developed, where the former aims at identifying and quantifying the e-learner experience and the latter represents a well-defined evolutionary plateau towards achieving a mature e-learning process from a technological perspective. Both models have been combined with a new developed data-driven Validation and Verification Model to develop a Concern-based Evaluation Approach for e-Learning artefacts, which is considered as another contribution