Presents the 2019 subject/author index for this publication.
WepSIM: An Online Interactive Educational Simulator Integrating Microdesign, Microprogramming, and Assembly Language Programming
Our educational project has three primary goals. First, we want to provide a robust vision of how hardware and software interplay, by integrating the design of an instruction set (through microprogramming) and using that instruction set for assembly programming. Second, we wish to offer a versatile and interactive tool where the previous integrated vision could be tested. The tool we have developed to achieve this is called WepSIM and it provides the view of an elemental processor together with a microprogrammed subset of the MIPS instruction set. In addition, WepSIM is flexible enough to be adapted to other instruction sets or hardware components (e.g., ARM or x86). Third, we want to extend the activities of our university courses, labs, and lectures (fixed hours in a fixed place), so that students may learn by using their mobile device at any location, and at any time during the day. This paper presents how WepSIM has improved the teaching of Computer Architecture courses by empowering students with a more dynamic and guided learning process. In this paper, we show the results obtained during the experience of using the simulator in the Computer Structure course of the Bachelor's Degree in Computer Science and Engineering, University Carlos III of Madrid.
Children with autism spectrum disorder (ASD) are characterized by deficits in social communication, partly attributed to the inability to pick up cues from social partners using joint attention (JA) skill. These deficits have cascading adverse effects on language acquisition and the development of cognitive skills. Therapist-mediated JA interventions are labor intensive. Robot-facilitated skill training is expensive, need specialized knowledge to operate, etc. In contrast, computer-based JA skill training platforms are affordable, offers flexibility to the designer, but lack individualization. Individualization is critical for effective skill training. To bridge this gap, we have developed virtual reality based JA task platform augmented with hierarchical prompt protocol (using eye, head turn, finger pointing, and sparkling cues). It was adaptive to individualized performance and autonomously increased level of prompting on demand. Results of a study with 20 pairs of age-matched ASD and typically developing (TD) participants indicate the potential of our system to identify JA skill deficits. Participants with ASD showed impairment in following eye cue. However, their ability to pick up finger pointing was the best, with some of them being able to pick up only the sparkling cue. However, all TD participants were able to pick up eye cue with none requiring other cues.
Bullying is a serious social problem at schools, very prevalent independently of culture and country, and particularly acute for teenagers. With the irruption of always-on communications technology, the problem, now termed cyberbullying, is no longer restricted to school premises and hours. There are many different approaches to address cyberbullying, such as school buddies, educational videos, or involving police in counseling; however, awareness continues to be insufficient. We have developed Conectado, a serious game to be used in the classroom to increase awareness on bullying and cyberbullying in schools. While playing the game, students gain a first-hand immersive experience of the problem and the associated emotions, fostering awareness and empathy with victims. This paper describes Conectado and presents its validation with actual students using game analytics.
Intelligent learning environments can be designed to support the development of learners’ cognitive skills, strategies, and metacognitive processes as they work on complex decision-making and problem-solving tasks. However, the complexity of the tasks may impede the progress of novice learners. Providing adaptive feedback to learners who face difficulties requires learner modeling approaches that can identify learners’ proficiencies and the difficulties they face in executing required skills, strategies, and metacognitive processes. This paper discusses a multilevel hierarchical learner modeling scheme that analyzes and captures learners’ cognitive processes and problem-solving strategies along with their performance on assigned tasks in a game-based environment called UrbanSim that requires complex decision making for dealing with counterinsurgency scenarios. As the scenario evolves in a turn-by-turn fashion, UrbanSim evaluates the learners’ moves using a number of performance measures. Our learner modeling scheme interprets the reported performance values by analyzing the learners’ activities captured in log files to derive learners’ proficiencies in associated cognitive skills and strategies, and updates the learner model. We discuss the details of the learner modeling algorithms in this paper, and then demonstrate the effectiveness of our approach by presenting results from a study we conducted at Vanderbilt University.
This paper studies students engagement in e-learning environments in which students work independently and solve problems without external supervision. We propose a new method to infer engagement patterns of users in such self-directed environments. We view engagement as a continuous process in time, measured along chosen axes that are derived from student data in the system using unsupervised learning (Principal Component Analysis). We construct a trajectory of user activity by projecting the user's scores along the selected PCs at regular time intervals. This approach is applied to a popular e-learning software for K12 math education that is used by thousands of students worldwide. We identify cohorts of users according to the way their trajectory changes over time (e.g., monotone up, monotone down, and constant). Each of the cohorts exhibits distinct behavioral dynamics and differed substantially in the amount of time users spent in the e-learning system. Specifically, one cohort included students that dropped out of the system after choosing very difficult problems that they were not able to complete, while another cohort included students users that chose more diverse problems and stayed longer in the system. In future work, these results can be used by teachers or intelligent tutors to track students’ engagement in the system and decide whether and how to intervene.
Learning design in a massive open online course (MOOC) intends to promote creativity, autonomy, and social networked learning, amongst other things. Students in a MOOC are required to self-regulate their learning to properly self-monitor their learning process and effectiveness of the adopted learning strategies. This paper presents the results of a study among 279 students enrolled in a MOOC that was enriched with a set of scaffolding interventions for social mirroring. The mirroring interventions supported social awareness and social embeddedness of learners. Associations between the use of the interventions and microlevel self-regulated learning processes were measured and analyzed. The extent to which those associations are affected by learner demographics and motivational characteristics was also investigated. Findings show that interventions that provide students, throughout the course, with learning updates and progress of peers are associated with the students' engagement with learning tasks and applying changes in strategies for completing those tasks. Social awareness scaffold influenced more students low in need for cognition, with a higher education degree, high in performance-approach orientation and low in grit, to engage with their learning tasks, while its effect on the change in learning strategies was higher with those early and towards the end of their careers and high in performance-approach strategy. The social comparison scaffold affected more students low in mastery goal orientation and high in grit to work on their learning tasks.
Learning From Worked Examples, Erroneous Examples, and Problem Solving: Toward Adaptive Selection of Learning Activities
Problem solving, worked examples, and erroneous examples have proven to be effective learning activities in Intelligent Tutoring Systems (ITSs). However, it is generally unknown how to select learning activities adaptively in ITSs to maximize learning. In the previous work of A. Shareghi Najar and A. Mitrovic, alternating worked examples with problem solving (AEP) was found to be superior to learning only from worked examples or only from problem solving. In our first study, we investigated whether the addition of erroneous examples further improves learning in comparison to AEP. The results indicated that erroneous examples prepared students better for problem solving in comparison to worked examples. Explaining and correcting erroneous examples also led to improved debugging and problem-solving skills. In the second study, we introduced a novel strategy that adaptively decided what learning activity (a worked example, a 1-error erroneous example, a 2-error erroneous example, or a problem to be solved) is appropriate for a student based on his/her performance. We found the adaptive strategy resulted in comparative learning improvement in comparison to the fixed sequence of worked/erroneous examples and problem solving, but with a significantly lower number of learning activities.
We present the design of a citizen science system that uses machine learning to guide the presentation of image classification tasks to newcomers to help them more quickly learn how to do the task while still contributing to the work of the project. A Bayesian model for tracking volunteer learning for training with tasks with uncertain outcomes is presented and fit to data from 12,986 volunteer contributors. The model can be used both to estimate the ability of volunteers and to decide the classification of an image. A simulation of the model applied to volunteer promotion and image retirement suggests that the model requires fewer classifications than the current system.
Maintaining and cultivating student engagement is critical for learning. Understanding factors affecting student engagement can help in designing better courses and improving student retention. The large number of participants in massive open online courses (MOOCs) and data collected from their interactions on the MOOC open up avenues for studying student engagement at scale. In this work, we develop an interpretable statistical relational learning model for understanding student engagement in online courses using a complex combination of behavioral, linguistic, structural, and temporal cues. We show how to abstract student engagement types of active, passive, and disengagement as meaningful latent variables using logical rules in our model connecting student behavioral signals with student success in MOOCs. We demonstrate that the latent formulation for engagement helps in predicting two measures of student success: performance, their final grade in the course, and survival, their continued presence in the course till the end, across seven MOOCs. Further, in order to initiate better instructor interventions, we need to be able to predict student success early in the course. We demonstrate that we can predict student success early in the course reliably using the latent model. We also demonstrate the utility of our models in predicting student success in new courses, by training our models on one course and testing on another course. We show that the latent abstractions are helpful in predicting student success and engagement reliably in new MOOCs that haven't yet gathered student interaction data. We then perform a closer quantitative analysis of different features derived from student interactions on the MOOC and identify student activities that are good indicators of student success at different points in the course. Through a qualitative analysis of the latent engagement variable values, we demonstrate their utility in understanding students&- x0027; engagement levels at various points in the course and movement of students across different types of engagement.
Group Optimization to Maximize Peer Assessment Accuracy Using Item Response Theory and Integer Programming
With the wide spread large-scale e-learning environments such as MOOCs, peer assessment has been popularly used to measure the learner ability. When the number of learners increases, peer assessment is often conducted by dividing learners into multiple groups to reduce the learner's assessment workload. However, in such cases, the peer assessment accuracy depends on the method of forming groups. To resolve that difficulty, this study proposes a group formation method to maximize peer assessment accuracy using item response theory and integer programming. Experimental results, however, have demonstrated that the method does not present sufficiently higher accuracy than a random group formation method does. Therefore, this study further proposes an external rater assignment method that assigns a few outside-group raters to each learner after groups are formed using the proposed group formation method. Through results of simulation and actual data experiments, this study demonstrates that the proposed external rater assignment can substantially improve peer assessment accuracy.
Feature Engineering and Ensemble-Based Approach for Improving Automatic Short-Answer Grading Performance
In this paper, we studied different automatic short answer grading (ASAG) systems to provide a comprehensive view of the feature spaces explored by previous works. While the performance reported in previous works have been encouraging, systematic study of the features is lacking. Apart from providing systematic feature space exploration, we also presented ensemble methods that have been experimentally validated to exhibit significantly higher grading performance over the existing papers in almost all the datasets in ASAG domain. A comparative study over different features and regression models toward short-answer grading has been performed with respect to evaluation metrics used in evaluating ASAG. Apart from traditional text similarity based features like WordNet similarity, latent semantic analysis, and others, we have introduced novel features like topic models suited for short text, relevance feedback based features. An ensemble-based model has been built using a combination of different regression models with an approach based on stacked regression. The proposed ASAG has been tested on the University of North Texas dataset for the regression task, whereas in case of classification task, the student response analysis (SRA) based ScientsBank and Beetle corpus have been used for evaluation. The grading performance in case of ensemble-based ASAG is highly boosted from that exhibited by an individual regression model. Extensive experimentation has revealed that feature selection, introduction of novel features, and regressor stacking have been instrumental in achieving considerable improvement in performance over the existing methods in ASAG domain.
Humor and fear appeals are widely employed in traditional communication for educational purposes, but their exploitation in animated pedagogical agents has been scarcely explored. We studied the use of humor and fear appeals by a three-dimensional animated pedagogical agent that taught the same procedural knowledge in four conditions: i) humor appeal, ii) fear appeal, iii) humor, and fear appeal together, and iv) no emotion appeals. The agent delivered and illustrated the knowledge in an application for aviation safety education. The results of our study show that using the educational application had overall positive learning effects, regardless of the appeals used by the agent. Resort to humor and fear appeals by the agent affected instead participants’ mood, liking of the application, and perception of the agent. The paper includes a discussion of the advantages and disadvantages of using humor and fear appeals in animated pedagogical agents.
Effects of a Ubiquitous Guide-Learning System on Cultural Heritage Course Students’ Performance and Motivation
In most cultural heritage courses, students physically visit several renowned heritage sites for educational purposes. However, because of time and manpower limitations, many teachers use traditional outdoor instruction methods to transmit vital information regarding these sites and buildings. This approach could result in students merely memorizing knowledge for test-taking purposes, rather than engaging in active thinking. Therefore, this study proposed using a ubiquitous guide-learning system to enhance students’ performance and active participation in cultural heritage courses. To evaluate the effectiveness of the proposed system, a trial experiment was conducted to visit eight heritage sites featured in the cultural heritage course offered by a local university. In total, 62 students were randomly assigned to either an experimental group testing the proposed system or a control group using traditional outdoor instruction methods. The experimental results indicated that our ubiquitous guide-learning system significantly improved students’ learning achievements. The outcome of the Instructional Materials Motivation Survey questionnaire indicated that the experimental group participants were motivated to learn and interested in using the proposed system. Moreover, the two groups had statistically significant results for the attention, confidence, and satisfaction factors. Therefore, implementing our proposed system in conjunction with cultural heritage education courses may yield a significant learning advantage for students by improving overall learning performance and motivation.
Comprehensive Analysis of Discussion Forum Participation: From Speech Acts to Discussion Dynamics and Course Outcomes
Learning in computer-mediated setting represents a complex, multidimensional process. This complexity calls for a comprehensive analytical approach that would allow for understanding of various dimensions of learner generated discourse and the structure of the underlying social interactions. Current research, however, primarily focuses on manual or, more recently, supervised methods for discourse analysis. Moreover, discourse and social structures are typically analyzed separately without the use of computational methods that can offer a holistic perspective. This paper proposes an approach that addresses these two challenges, first, by using an unsupervised machine learning approach to extract speech acts as representations of knowledge construction processes and finds transition probabilities between speech acts across different messages, and second, by integrating the use of discovered speech acts to explain the formation of social ties and predicting course outcomes. We extracted six categories of speech acts from messages exchanged in discussion forums of two MOOCs and each category corresponded to knowledge construction processes from well-established theoretical models. We further showed how measures derived from discourse analysis explained the ways how social ties were created that framed emerging social networks. Multiple regression models showed that the combined use of measures derived from discourse analysis and social ties predicted learning outcomes.
Adaptive e-learning can be used to personalize learning environment for students to meet their individual demands. Individual differences depend on the students’ personality traits. Numerous studies have indicated that understanding the role of personality in the learning process can facilitate learning. Hence, personality identification in e-learning is a critical issue in education. In this study, we propose the enhanced extended nearest neighbor (EENN) algorithm to automatically identify two of the Big Five personality traits from students’ behavior in online learning: openness to experience and extraversion. The performance of the proposed method is evaluated using a fivefold cross-validation approach on data from 662 senior high school students. The experimental results indicate that the EENN method can automatically recognize personality at an average accuracy of 0.758. The optimized method that combines EENN with particle swarm optimization significantly improves the identification, resulting in an average accuracy of 0.976. The results can benefit students by increasing the accuracy of personalization based on their personality traits, while simultaneously allowing them to be better understood and possibly allowing their instructors to provide more appropriate learning interventions.
Automatic multiple choice question (MCQ) generation from a text is a popular research area. MCQs are widely accepted for large-scale assessment in various domains and applications. However, manual generation of MCQs is expensive and time-consuming. Therefore, researchers have been attracted toward automatic MCQ generation since the late 90's. Since then, many systems have been developed for MCQ generation. We perform a systematic review of those systems. This paper presents our findings on the review. We outline a generic workflow for an automatic MCQ generation system. The workflow consists of six phases. For each of these phases, we find and discuss the list of techniques adopted in the literature. We also study the evaluation techniques for assessing the quality of the system generated MCQs. Finally, we identify the areas where the current research focus should be directed toward enriching the literature.
We present a system that automatically generates deictic gestures for animated pedagogical agents (APAs). The system takes audio and text as input, which define what the APA has to say, and generates animated gestures based on a set of rules. The automatically generated gestures point to the exact locations of elements on a whiteboard nearby the APA, which are calculated by searching for keywords mentioned in the speech. We conducted a study with 100 subjects, in which we compared lecture videos containing gestures automatically-scripted by the system to videos of the same lecture containing manually-scripted gestures. The study results show that the manually-scripted and automatically-scripted lectures had comparable number of gestures, and that the gestures were timed equally well.
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