An intelligent tutoring module controlled by BDI agents for an e-learning platform

INES (INtelligent Educational System) is an operative prototype of an e-learning platform. This platform includes several tools and technologies, such as: (i) semantic management of users and contents; (ii) conversational agents to communicate with students in natural language; (iii) BDI-based (Believes, Desires, Intentions) agents, which shape the tutoring module of the system; (iv) an inference engine; and (v) ontologies, to semantically model the users, their activities, and the learning contents. The main contribution of this paper is the intelligent tutoring module of the system. Briefly, the tasks of this module are to recognize each student (checking his/her system credentials) and to obtain information about his/her learning progress. So, it can be able to suggest to each student specific tasks to achieve his/her particular learning objectives, based on several parameters related to the existing learning paths and the student’s profile.     

Usability of affective interfaces for a digital arts tutoring system

Affective computing techniques have become increasingly important as advanced education technologies. By applying these techniques to education, this work designs and evaluates a novel Affective Tutoring System for the Digital Arts (ATSDAs). By semantically analysing a text with ontological references, the emotions induced by a text when input by a user are identified. Inference of emotions is accomplished using OMCSNet and WordNet, two engines commonly used in computational linguistics research. The proposed system has a visual agent that provides text feedback based on inferred emotions from textual analysis. The proposed system has a conscientious design flow that includes concept modelling, prototype design, expert-based evaluation (which consists of a cognitive walkthrough and heuristic evaluation), final system design and a series of evaluations from a learner's perspective. The System Usability Scale (SUS) evaluation results show that this system achieves positive usability and learners enjoy interacting with the proposed system.     

Automatic summary assessment for intelligent tutoring systems

Summary writing is an important part of many English Language Examinations. As grading students’ summary writings is a very time-consuming task, computer-assisted assessment will help teachers carry out the grading more effectively. Several techniques such as latent semantic analysis (LSA), n-gram co-occurrence and BLEU have been proposed to support automatic evaluation of summaries. However, their performance is not satisfactory for assessing summary writings. To improve the performance, this paper proposes an ensemble approach that integrates LSA and n-gram co-occurrence. As a result, the proposed ensemble approach is able to achieve high accuracy and improve the performance quite substantially compared with current techniques. A summary assessment system based on the proposed approach has also been developed.     

Automatic program debugging for intelligent tutoring systems

Program debugging is an important part of the domain expertise required for intelligent tutoring systems that teach programming languages. This article explores the process by which student programs can be automatically debugged in order to increase the instructional capabilities of these systems. The research presented provides a methodology and implementation for the diagnosis and correction of nontrivial recursive programs. In this approach, recursive programs are debugged by repairing induction proofs in the Boyer-Moore logic. The induction proofs constructed and debugged assert the computational équivalence of student programs to correct exemplar solutions. Exemplar solutions not only specify correct implementations but also provide correct code to replace buggy student code. Bugs in student code are repaired with heuristics that attempt to minimize the scope of repair. The automated debugging of student code is greatly complicated by the tremendous variability that arises in student solutions to nontrivial tasks. This variability can be coped with, and debugging performance improved, by explicit reasoning about computational semantics during the debugging process. This article supports these claims by discussing the design, implementation, and evaluation of Talus, an automatic debugger for LISP programs, and by examining related work in automated program debugging. Talus relies on its abilities to reason about computational semantics to perform algorithm recognition, infer code teleology, and to automatically detect and correct nonsyntactic errors in student programs written in a restricted, but nontrivial, subset of LISP. Solutions can vary significantly in algorithm, functional decomposition, role of variables, data flow, control flow, values returned by functions, LISP primitives used, and identifiers used. Solutions can consist of multiple functions, each containing multiple bugs. Empiricial evaluation demonstrates that Talus achieves high performance in debugging widely varying student solutions to challenging tasks.     

基于情感识别的智能教学系统研究

针对传统的智能教学系统(ITS)在情感方面的缺失,提出了基于情感识别技术的ITS模型.该系统模型在传统的教学系统上新增情感识别模块,利用人脸表情识别以及文本识别等技术所构建,可以获取和识别学生的学习情感,并根据学习情感进行相应的情感激励策略,实现情感化的教学.     

Automatic generation of tetrahedral meshes from medical images

A new tetrahedral meshing algorithm from the series of medical images is proposed. Sectional contours are extracted from medical images, and by the use of correspondence, tiling, and branching process, the side surfaces between sections are triangulated in addition to the triangulation on each section. As for the mesh generation for an object between two sections, an advancing front algorithm is employed to generate tetrahedral elements by using basic operators. Sample meshes are constructed from medical images for finite element analysis of biomechanical models.     

Fuzzy model of dominance emotions in affective computing

To date, most of the human emotion recognition systems are intended to sense the emotions and their dominance individually. This paper discusses a fuzzy model for multilevel affective computing based on the dominance dimensional model of emotions. This model can detect any other possible emotions simultaneously at the time of recognition. One hundred and thirty volunteers from various countries with different cultural backgrounds were selected to record their emotional states. These volunteers have been selected from various races and different geographical locations. Twenty-seven different emotions with their strengths in a scale of 5 were questioned through a survey. Recorded emotions were analyzed with the other possible emotions and their levels of dominance to build the fuzzy model. Then this model was integrated into a fuzzy emotion recognition system using three input devices of mouse, keyboard and the touch screen display. Support vector machine classifier detected the other possible emotions of the users along with the directly sensed emotion. The binary system (non-fuzzy) sensed emotions with an incredible accuracy of 93 %. However, it only could sense limited emotions. By integrating this model, the system was able to detect more possible emotions at a time with slightly lower recognition accuracy of 86 %. The recorded false positive rates of this model for four emotions were measured at 16.7 %. The resulted accuracy and its false positive rate are among the top three accurate human emotion recognition (affective computing) systems.     

Automatic detection of learning styles for an e-learning system

A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user’s browsing history and knowledge factors like user’s prior knowledge. In this paper, we address the problem of extracting the learner model based on Felder–Silverman learning style model. The target learners in this problem are the ones studying basic science. Using NBTree classification algorithm in conjunction with Binary Relevance classifier, the learners are classified based on their interests. Then, learners’ learning styles are detected using these classification results. Experimental results are also conducted to evaluate the performance of the proposed automated learner modeling approach. The results show that the match ratio between the obtained learner’s learning style using the proposed learner model and those obtained by the questionnaires traditionally used for learning style assessment is consistent for most of the dimensions of Felder–Silverman learning style.     

Automatic tutoring system to support cross-disciplinary training in Big Data

During the last decade, Big Data has emerged as a powerful alternative to address latent challenges in scalable data management. The ever-growing amount and rapid evolution of tools, techniques, and technologies associated to Big Data require a broad skill set and deep knowledge of several domains—ranging from engineering to business, including computer science, networking, or analytics among others—which complicate the conception and deployment of academic programs and methodologies able to effectively train students in this discipline. The purpose of this paper is to propose a learning and teaching framework committed to train masters’ students in Big Data by conceiving an intelligent tutoring system aimed to (1) automatically tracking students’ progress, (2) effectively exploiting the diversity of their backgrounds, and (3) assisting the teaching staff on the course operation. Obtained results endorse the feasibility of this proposal and encourage practitioners to use this approach in other domains.

     

Automatic inference of complex affective states

Affective states and their non-verbal expressions are an important aspect of human reasoning, communication and social life. Automated recognition of affective states can be integrated into a wide variety of applications for various fields. Therefore, it is of interest to design systems that can infer the affective states of speakers from the non-verbal expressions in speech, occurring in real scenarios. This paper presents such a system and the framework for its design and validation. The framework defines a representation method that comprises a set of affective-state groups or archetypes that often appear in everyday life. The inference system is designed to infer combinations of affective states that can occur simultaneously and whose level of expression can change over time. The framework considers also the validation and generalisation of the system. The system was built of 36 independent pair-wise comparison machines, with average accuracy (tenfold cross-validation) of 75%. The accumulated inference system yielded total accuracy of 83% and recognised combinations for different nuances within the affective-state groups. In addition to the ability to recognise these affective-state groups, the inference system was applied to characterisation of a very large variety of affective state concepts (549 concepts) as combinations of the affective-state groups. The system was also applied to annotation of affective states that were naturally evoked during sustained human–computer interactions and multi-modal analysis of the interactions, to new speakers and to a different language, with no additional training. The system provides a powerful tool for recognition, characterisation, annotation (interpretation) and analysis of affective states. In addition, the results inferred from speech in both English and Hebrew, indicate that the vocal expressions of complex affective states such as thinking, certainty and interest transcend language boundaries.     

Automatic code generation for GPUs in llc

llc is a C-based language where parallelism is expressed using compiler directives. In this paper, we present a new backend of an llc compiler that produces code for GPUs. We have also implemented a software architecture that eases the development of new backends. Our design represents an intermediate layer between a high-level parallel language and different hardware architectures.     

Tune in to your emotions: a robust personalized affective music player

The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners’ personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application.     

Clinical reasoning gains in medical PBL: an UMLS based tutoring system

Problem based learning is becoming widely popular as an effective teaching method in medical education. Paying individual attention to a small group of students in medical problem-based learning (PBL) can place burden on the workload of medical faculty whose time is very costly. Intelligent tutoring systems offer a cost effective alternative in helping to train the students, but they are typically prone to brittleness and the knowledge acquisition bottleneck. Existing tutoring systems accept a small set of approved solutions for each problem scenario stored into the system. Plausible student solutions that lie outside the scope of the explicitly encoded ones receive little acknowledgment from the system. Tutoring hints are also confined to the knowledge space of the approved solutions, leading to brittleness in the tutoring approach. We report the clinical reasoning gains off a tutoring system for medical PBL that employs and represents the widely available medical knowledge source UMLS as the domain ontology. We exploit the structure of the concept hierarchy to expand the plausible solution space and generate hints based on the problem solving context. Evaluation of student learning outcomes led to highly significant learning gains (Mann-Whitney, p < 0.001).     

Combined inner and outer loop feedback in an intelligent tutoring system for statistics in higher education

Intelligent tutoring systems (ITSs) can provide inner loop feedback about steps within tasks, and outer loop feedback about performance on multiple tasks. While research typically addresses these feedback types separately, many ITSs offer them simultaneously. This study evaluates the effects of providing combined inner and outer loop feedback on social sciences students' learning process and performance in a first-year university statistics course. In a 2 x 2 factorial design (elaborate inner loop vs. minimal inner loop and outer loop vs. no outer loop feedback) with 521 participants, the effects of both feedback types and their combination were assessed through multiple linear regression models. Results showed mixed effects, depending on students' prior knowledge and experience, and no overall effects on course performance. Students tended to use outer loop feedback less when also receiving elaborate inner loop feedback. We therefore recommend introducing feedback types one by one and offering them for substantial periods of time.     

Automatic feature generation for handwritten digit recognition

An automatic feature generation method for handwritten digit recognition is described. Two different evaluation measures, orthogonality and information, are used to guide the search for features. The features are used in a backpropagation trained neural network. Classification rates compare favorably with results published in a survey of high-performance handwritten digit recognition systems. This classifier is combined with several other high performance classifiers. Recognition rates of around 98% are obtained using two classifiers on a test set with 1000 digits per class     

Automatic fuzzy ontology generation for semantic Web

Ontology is an effective conceptualism commonly used for the semantic Web. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain can be a difficult and tedious task. To tackle this problem, this paper proposes the FOGA (fuzzy ontology generation framework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: fuzzy formal concept analysis, concept hierarchy generation, and fuzzy ontology generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. Finally, a fuzzy-based technique for integrating other attributes of database to the ontology is proposed.     

Automatic route planning for GPS art generation

Computational Visual Media - In this paper, we present a novel approach to automated route generation of global positioning system (GPS) artwork. The term GPS artwork describes the generation of...