This page has only limited features, please log in for full access.
This study investigated the effect of two technology-enhanced learning methods (an adaptive educational computer game vs. a PowerPoint presentation with multimedia) on learners with different prior learning attitudes and knowledge regarding skills, conceptual knowledge, and overall knowledge of computational thinking (CT). Data from 70 students aged 11–12 were analysed using factorial multivariate analysis of covariance and analysis of covariance. Our findings revealed that while effective in fostering conceptual knowledge of CT (such as sequence, loop, and conditional), the conventional technology-enhanced learning approach falls short when it comes to promoting CT skills (such as pattern recognition and debugging). The game approach, however, could simultaneously promote CT skills and conceptual knowledge, leading to improvement in the overall knowledge of CT. Additional analysis showed that learners with different prior knowledge and learning attitudes made a higher improvement using the adaptive game in terms of skills, conceptual knowledge, and overall knowledge of CT, especially for medium and lower prior knowledge learners that did not have a very high prior learning attitude. Consequently, to promote CT in primary school students, educators can employ computer game approaches that not only can foster both skills and conceptual knowledge of CT but also more effectively assist students with lower prior learning attitudes and knowledge.
Danial Hooshyar. Effects of technology‐enhanced learning approaches on learners with different prior learning attitudes and knowledge in computational thinking. Computer Applications in Engineering Education 2021, 1 .
AMA StyleDanial Hooshyar. Effects of technology‐enhanced learning approaches on learners with different prior learning attitudes and knowledge in computational thinking. Computer Applications in Engineering Education. 2021; ():1.
Chicago/Turabian StyleDanial Hooshyar. 2021. "Effects of technology‐enhanced learning approaches on learners with different prior learning attitudes and knowledge in computational thinking." Computer Applications in Engineering Education , no. : 1.
While modelling students’ learning behavior or preferences has been found as a crucial indicator for their course achievement, very few studies have considered it in predicting achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students’ learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students’ feature vectors and behavior model constitute a comprehensive students’ learning behavioral pattern which is later used for prediction of their course achievement. Lastly, using a multiple criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students’ achievement in courses with different numbers of students and features, showing the stability of the approach. Decision Tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students’ course achievement with a high accuracy through modelling their learning behavior during online courses.
Danial Hooshyar; YeongWook Yang. Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern. Complexity 2021, 2021, 1 -12.
AMA StyleDanial Hooshyar, YeongWook Yang. Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern. Complexity. 2021; 2021 ():1-12.
Chicago/Turabian StyleDanial Hooshyar; YeongWook Yang. 2021. "Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern." Complexity 2021, no. : 1-12.
Despite the success of Learning Analytics (LA), there are two obstacles to its application in educational games, including transparency in assessing educational outcomes in real-time gameplay, and clarity in representing those results to players. Open learner model (OLM) is a valuable instrument with capability to improve learning that meets such challenges. However, OLMs usually suffer issues concerning interactivity and transparency, which mostly regard the assessment mechanism that is used to evaluate learners’ knowledge. Tackling down transparency issues would offer context for interpreting and comparing learner model information, as well as promoting interactivity. As there is lack of studies investigating the potential of OLMs in educational games, we argue that this work can provide a valuable starting point for applying OLMs or adaptive visualizations of players’ learner models within gameplay sessions, which, in turn, can help to address both issues of application of LA to game research and OLMs. As a case study, we introduce the proposed approach into our adaptive computational thinking game.
Danial Hooshyar; Emanuele Bardone; Nour El Mawas; YeongWook Yang. Transparent Player Model: Adaptive Visualization of Learner Model in Educational Games. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 349 -357.
AMA StyleDanial Hooshyar, Emanuele Bardone, Nour El Mawas, YeongWook Yang. Transparent Player Model: Adaptive Visualization of Learner Model in Educational Games. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():349-357.
Chicago/Turabian StyleDanial Hooshyar; Emanuele Bardone; Nour El Mawas; YeongWook Yang. 2020. "Transparent Player Model: Adaptive Visualization of Learner Model in Educational Games." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 349-357.
Educational games have been increasingly used to improve students’ computational thinking. However, most existing games have focused on the theoretical knowledge of computational thinking, ignoring the development of computational thinking skills. Moreover, there is a lack of integration of adaptivity into educational computer games for computational thinking, which is crucial to addressing individual needs in developing computational thinking skills. In this study, we present an adaptive educational computer game, called AutoThinking, for developing students’ computational thinking skills in addition to their conceptual knowledge. To evaluate the effects of the game, we conducted an experimental study with 79 elementary school students in Estonia, where the experimental group learned with AutoThinking, while the control group used a traditional technology-enhanced learning approach. Our findings show that learning with the adaptive educational computer game significantly improved students’ computational thinking related to both conceptual knowledge and skills. Moreover, students using the adaptive educational computer game showed a significantly higher level of interest, satisfaction, flow state, and technology acceptance in learning computational thinking. Implications of the findings are also discussed.
Danial Hooshyar; Margus Pedaste; YeongWook Yang; Liina Malva; Gwo-Jen Hwang; Minhong Wang; Heuiseok Lim; Dejan Delev. From Gaming to Computational Thinking: An Adaptive Educational Computer Game-Based Learning Approach. Journal of Educational Computing Research 2020, 59, 383 -409.
AMA StyleDanial Hooshyar, Margus Pedaste, YeongWook Yang, Liina Malva, Gwo-Jen Hwang, Minhong Wang, Heuiseok Lim, Dejan Delev. From Gaming to Computational Thinking: An Adaptive Educational Computer Game-Based Learning Approach. Journal of Educational Computing Research. 2020; 59 (3):383-409.
Chicago/Turabian StyleDanial Hooshyar; Margus Pedaste; YeongWook Yang; Liina Malva; Gwo-Jen Hwang; Minhong Wang; Heuiseok Lim; Dejan Delev. 2020. "From Gaming to Computational Thinking: An Adaptive Educational Computer Game-Based Learning Approach." Journal of Educational Computing Research 59, no. 3: 383-409.
Several studies have reported that adaptivity and personalization in educational computer games facilitate reaching their full educational potential. However, there is little effort to develop adaptive educational computer games for promoting students' computational thinking (CT). In this study, an adaptive computer game is introduced, called AutoThinking, that not only promotes both CT skills and conceptual knowledge, but also provides adaptivity in both game-play and learning processes. To evaluate the possible effects of the game, an experimental study was carried out with 79 students in an elementary school in Estonia. AutoThinking and a conventional technology-enhanced learning approach were used for teaching CT to the experimental and control group, respectively. Our results reveal that AutoThinking improved students’ CT skills and conceptual knowledge better than the conventional approach. It was also found that students with a low and high level of prior knowledge made higher improvement in knowledge gain using the adaptive game compared to the traditional approach, especially those students with lower prior knowledge. Finally, our findings show that the adaptive game could also improve students' learning attitude toward CT better than the conventional approach, especially those students with higher prior learning attitudes.
Danial Hooshyar; Liina Malva; YeongWook Yang; Margus Pedaste; Minhong Wang; Heuiseok Lim. An adaptive educational computer game: Effects on students' knowledge and learning attitude in computational thinking. Computers in Human Behavior 2020, 114, 106575 .
AMA StyleDanial Hooshyar, Liina Malva, YeongWook Yang, Margus Pedaste, Minhong Wang, Heuiseok Lim. An adaptive educational computer game: Effects on students' knowledge and learning attitude in computational thinking. Computers in Human Behavior. 2020; 114 ():106575.
Chicago/Turabian StyleDanial Hooshyar; Liina Malva; YeongWook Yang; Margus Pedaste; Minhong Wang; Heuiseok Lim. 2020. "An adaptive educational computer game: Effects on students' knowledge and learning attitude in computational thinking." Computers in Human Behavior 114, no. : 106575.
Despite an increasing consensus regarding the significance of properly identifying the most suitable clustering method for a given problem, a surprising amount of educational research, including both educational data mining (EDM) and learning analytics (LA), neglects this critical task. This shortcoming could in many cases have a negative impact on the prediction power of both the EDM and LA based approaches. To address such issues, this work proposes an evaluation approach that automatically compares several clustering methods using multiple internal and external performance measures on 9 real-world educational datasets of different sizes, created from the University of Tartu’s Moodle system, to produce two-way clustering. Moreover, to investigate the possible effect of normalization on the performance of the clustering algorithms, this work performs the same experiment on a normalized version of the datasets. Since such an exhaustive evaluation includes multiple criteria, the proposed approach employs a multiple criteria decision-making method (i.e., TOPSIS) to rank the most suitable methods for each dataset. Our results reveal that the proposed approach can automatically compare the performance of the clustering methods and accordingly recommend the most suitable method for each dataset. Furthermore, our results show that in both normalized and nonnormalized datasets of different sizes with 10 features, DBSCAN and k-medoids are the best clustering methods, whereas agglomerative and spectral methods appear to be among the most stable and highly performing clustering methods for such datasets with 15 features. Regarding datasets with more than 15 features, OPTICS is among the top-ranked algorithms among the nonnormalized datasets, and k-medoids is the best among the normalized datasets. Interestingly, our findings reveal that normalization may have a negative effect on the performance of certain methods, e.g., spectral clustering and OPTICS; however, it appears to mostly have a positive impact on all of the other clustering methods.
Danial Hooshyar; YeongWook Yang; Margus Pedaste; Yueh-Min Huang. Clustering Algorithms in an Educational Context: An Automatic Comparative Approach. IEEE Access 2020, 8, 146994 -147014.
AMA StyleDanial Hooshyar, YeongWook Yang, Margus Pedaste, Yueh-Min Huang. Clustering Algorithms in an Educational Context: An Automatic Comparative Approach. IEEE Access. 2020; 8 (99):146994-147014.
Chicago/Turabian StyleDanial Hooshyar; YeongWook Yang; Margus Pedaste; Yueh-Min Huang. 2020. "Clustering Algorithms in an Educational Context: An Automatic Comparative Approach." IEEE Access 8, no. 99: 146994-147014.
Prediction of students' performance has been reported as a vital task which enables educators to take necessary actions to improve students’ learning. Numerous studies have concluded that students with lower procrastination tendencies archive more compared to those with higher procrastination tendencies. In this study, a new method is proposed to predict students’ procrastination tendencies discerned from their submission behavioural patterns in online learning. In this method, feature vectors signifying students’ submission patterns on homework are firstly drafted. Next, an ensemble clustering method is employed to optimally sort students into various categories of procrastination: procrastinator, procrastinator candidate, and non-procrastinator. Lastly, various classification methods are assessed to discern which one best predicts students’ procrastination tendencies. The efficacy of this approach is assessed through the data from a course comprised of 242 students at the University of Tartu in Estonia. Our study found that our method correctly identifies student procrastination from submission pattern data with 97% accuracy, and that the best performing classifier is linear support vector machine. Investigating the effect of different number of features (homework) on performance of clustering and classification methods indicate that finding the optimal number of feature to use in both clustering and classification methods is a vital task as it could potentially affect prediction power of our approach. More specifically, the results show that in our proposed approach, unlike clustering methods that show a better performance with lower number of features, classification methods mostly tend to show a better performance with larger number of features.
YeongWook Yang; Danial Hooshyar; Margus Pedaste; Minhong Wang; Yueh-Min Huang; Heuiseok Lim. Prediction of students’ procrastination behaviour through their submission behavioural pattern in online learning. Journal of Ambient Intelligence and Humanized Computing 2020, 1 -18.
AMA StyleYeongWook Yang, Danial Hooshyar, Margus Pedaste, Minhong Wang, Yueh-Min Huang, Heuiseok Lim. Prediction of students’ procrastination behaviour through their submission behavioural pattern in online learning. Journal of Ambient Intelligence and Humanized Computing. 2020; ():1-18.
Chicago/Turabian StyleYeongWook Yang; Danial Hooshyar; Margus Pedaste; Minhong Wang; Yueh-Min Huang; Heuiseok Lim. 2020. "Prediction of students’ procrastination behaviour through their submission behavioural pattern in online learning." Journal of Ambient Intelligence and Humanized Computing , no. : 1-18.
The open learner model (OLM) represents the knowledge or skill levels of learners in various ways, encouraging learners to actively participate in thinking about and crafting their own learning. Despite the important roles that OLMs play in higher education to support the learning process and self-regulated learning (SRL) in particular, there are few studies systematically reviewing OLM technology in higher education, and investigating their potential to foster self-regulated learning. Therefore, we carried out a systematic review of a 30-year sample of OLM studies in higher education and identified 64 articles that study the use of OLMs in supporting SRL. Our findings show that OLMs have been mainly used to support learners' cognition and a bit less metacognition and motivation; however, emotional support has been rarely provided. The most supported ones are Appraisal and Performance phases; Preparation of learning is enhanced by OLMs not so often. Although learners can edit or negotiate with their learning model in advanced ways, a simple inspectable OLM is more preferred. Reliance on unobservable nodes is less favored in modeling techniques in OLMs because such methods are highly dependent on expert authoring, thereby time-intensive and costly. Comparison and color-coding are two most-used features in OLMs, where the comparison feature is often used for enhancing learners’ engagement and motivation.
Danial Hooshyar; Margus Pedaste; Katrin Saks; Äli Leijen; Emanuele Bardone; Minhong Wang. Open learner models in supporting self-regulated learning in higher education: A systematic literature review. Computers & Education 2020, 154, 103878 .
AMA StyleDanial Hooshyar, Margus Pedaste, Katrin Saks, Äli Leijen, Emanuele Bardone, Minhong Wang. Open learner models in supporting self-regulated learning in higher education: A systematic literature review. Computers & Education. 2020; 154 ():103878.
Chicago/Turabian StyleDanial Hooshyar; Margus Pedaste; Katrin Saks; Äli Leijen; Emanuele Bardone; Minhong Wang. 2020. "Open learner models in supporting self-regulated learning in higher education: A systematic literature review." Computers & Education 154, no. : 103878.
A significant amount of research has indicated that students’ procrastination tendencies are an important factor influencing the performance of students in online learning. It is, therefore, vital for educators to be aware of the presence of such behavior trends as students with lower procrastination tendencies usually achieve better than those with higher procrastination. In the present study, we propose a novel algorithm—using student’s assignment submission behavior—to predict the performance of students with learning difficulties through procrastination behavior (called PPP). Unlike many existing works, PPP not only considers late or non-submissions, but also investigates students’ behavioral patterns before the due date of assignments. PPP firstly builds feature vectors representing the submission behavior of students for each assignment, then applies a clustering method to the feature vectors for labelling students as a procrastinator, procrastination candidate, or non-procrastinator, and finally employs and compares several classification methods to best classify students. To evaluate the effectiveness of PPP, we use a course including 242 students from the University of Tartu in Estonia. The results reveal that PPP could successfully predict students’ performance through their procrastination behaviors with an accuracy of 96%. Linear support vector machine appears to be the best classifier among others in terms of continuous features, and neural network in categorical features, where categorical features tend to perform slightly better than continuous. Finally, we found that the predictive power of all classification methods is lowered by an increment in class numbers formed by clustering.
Danial Hooshyar; Margus Pedaste; YeongWook Yang. Mining Educational Data to Predict Students’ Performance through Procrastination Behavior. Entropy 2019, 22, 12 .
AMA StyleDanial Hooshyar, Margus Pedaste, YeongWook Yang. Mining Educational Data to Predict Students’ Performance through Procrastination Behavior. Entropy. 2019; 22 (1):12.
Chicago/Turabian StyleDanial Hooshyar; Margus Pedaste; YeongWook Yang. 2019. "Mining Educational Data to Predict Students’ Performance through Procrastination Behavior." Entropy 22, no. 1: 12.
Computational thinking (CT) is gaining recognition as an important skill set for students, both in computer science and other disciplines. Digital computer games have proven to be attractive and engaging for fostering CT. Even though there are a number of promising studies of games that teach CT, most of these do not consider whether students are learning CT skills or adapt to individual players’ needs. Instead, they boost theoretical knowledge and promote student motivation in CT by usually following a computer-assisted instruction concept that is predefined and rigid, offering no adaptability to each student. To overcome such problems, by benefiting from a probabilistic model that deals with uncertainty, Bayesian Network (BN), we propose an adaptive CT game called AutoThinking. It seeks to engage players through personalized and fun game play while offering timely visualized hints, feedback, and tutorials which cues players to learn skills and concepts tailored to their abilities. The application of BN to AutoThinking not only adaptively provides multiple descriptions of learning materials (by offering adaptive textual, graphical, and video tutorials), similar to the natural way that teachers use in classrooms, but also creatively integrates adaptivity within gameplay by directing the cats (non-player characters) to a specific zone on the game according to players’ ability. Consequently, these adaptive features enable AutoThinking to engage players in an individually tailored gameplay and instill CT concepts and skills.
Danial Hooshyar; Heuiseok Lim; Margus Pedaste; Kisu Yang; Moein Fathi; YeongWook Yang. AutoThinking: An Adaptive Computational Thinking Game. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 381 -391.
AMA StyleDanial Hooshyar, Heuiseok Lim, Margus Pedaste, Kisu Yang, Moein Fathi, YeongWook Yang. AutoThinking: An Adaptive Computational Thinking Game. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():381-391.
Chicago/Turabian StyleDanial Hooshyar; Heuiseok Lim; Margus Pedaste; Kisu Yang; Moein Fathi; YeongWook Yang. 2019. "AutoThinking: An Adaptive Computational Thinking Game." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 381-391.
The main aim of the present study is to assess whether the open learner model (OLM) is capable of promoting students' active thinking by enhancing their self‐regulation in online higher education learning environments. To this aim, we systematically reviewed the literature of the last three decades and found 67 articles, of which only a sample of 15 were considered. Based on the findings, we performed a narrative analysis of the studies concerning technological features of OLMs that cater to the three main aspects concerning self‐regulated learning, namely, cognition, metacognition and motivation. Our analysis of the literature confirmed that these three aspects are all subject to some measure of influence. In mutually interacting, these three components support learners to reach a better understanding of their learning process. Specifically, it seems that mostly all three type of OLMs, inspectable, negotiable, and co‐operative, with simple and complex graphical presentation of their learner models and capacity to colour‐code and compare—alike appear optimal for augmenting cognition, metacognition, and motivation. They seemingly do so through offering a wealth of techniques pertaining to knowledge, difficulties, and misconception visualization. The results presented suggest that OLMs have a positive impact on learners' active thinking regarding their learning process.
Danial Hooshyar; Külli Kori; Margus Pedaste; Emanuele Bardone. The potential of open learner models to promote active thinking by enhancing self‐regulated learning in online higher education learning environments. British Journal of Educational Technology 2019, 50, 2365 -2386.
AMA StyleDanial Hooshyar, Külli Kori, Margus Pedaste, Emanuele Bardone. The potential of open learner models to promote active thinking by enhancing self‐regulated learning in online higher education learning environments. British Journal of Educational Technology. 2019; 50 (5):2365-2386.
Chicago/Turabian StyleDanial Hooshyar; Külli Kori; Margus Pedaste; Emanuele Bardone. 2019. "The potential of open learner models to promote active thinking by enhancing self‐regulated learning in online higher education learning environments." British Journal of Educational Technology 50, no. 5: 2365-2386.
One of the key tasks for recommender systems is the prediction of personalized sequential behavior. There are two primary means of modeling sequential patterns and long-term user preferences: Markov chains and matrix factorization, respectively. Together, they provide a unified approach to predicting user actions. In spite of their strengths in tackling dense data, however, these methods struggle with the sparsity issues often present in real-world datasets. In approaching this problem, we propose combining similarity-based methods (demonstrably helpful for sequentially unaware item recommendation) with Markov chains to offer individualized sequential recommendations. This approach, called GPS (a factorized group preference-based similarity model), further leverages the idea of group preference along with user preference to introduce a greater array of interactions between users—which in turn eases the problem of data sparsity and cold users and cuts down on the assumption of a strong independency within various factors. By applying our method to a range of large, real-world datasets, we demonstrate quantitatively that GPS outperforms several state-of-the-art methods, particularly in cases with sparse datasets. Regarding qualitative findings, GPS also grasps personalized interactions and can provide recommendations that are both on-target and meaningful.
YeongWook Yang; Danial Hooshyar; Heui Seok Lim. GPS: Factorized group preference-based similarity models for sparse sequential recommendation. Information Sciences 2019, 481, 394 -411.
AMA StyleYeongWook Yang, Danial Hooshyar, Heui Seok Lim. GPS: Factorized group preference-based similarity models for sparse sequential recommendation. Information Sciences. 2019; 481 ():394-411.
Chicago/Turabian StyleYeongWook Yang; Danial Hooshyar; Heui Seok Lim. 2019. "GPS: Factorized group preference-based similarity models for sparse sequential recommendation." Information Sciences 481, no. : 394-411.
Video scene segmentation is very important research in the field of computer vision, because it helps in efficient storage, indexing and retrieval of videos. Achieving this kind of scene segmentation cannot be done by just calculating the similarity of low-level features presented in the video; high-level features should also be considered to achieve a better performance. Even though much research has been conducted on video scene segmentation, most of these studies failed to semantically segment a video into scenes. Thus, in this study, we propose a Deep-learning Semantic-based Scene-segmentation model (called DeepSSS) that considers image captioning to segment a video into scenes semantically. First, the DeepSSS performs shot boundary detection by comparing colour histograms and then employs maximum-entropy-applied keyframe extraction. Second, for semantic analysis, using image captioning that benefits from deep learning generates a semantic text description of the keyframes. Finally, by comparing and analysing the generated texts, it assembles the keyframes into a scene grouped under a semantic narrative. That said, DeepSSS considers both low- and high-level features of videos to achieve a more meaningful scene segmentation. By applying DeepSSS to data sets from MS COCO for caption generation and evaluating its semantic scene-segmentation task results with the data sets from TRECVid 2016, we demonstrate quantitatively that DeepSSS outperforms other existing scene-segmentation methods using shot boundary detection and keyframes. What’s more, the experiments were done by comparing scenes segmented by humans and scene segmented by the DeepSSS. The results verified that the DeepSSS’ segmentation resembled that of humans. This is a new kind of result that was enabled by semantic analysis, which was impossible by just using low-level features of videos.
Hyesung Ji; Danial Hooshyar; Kuekyeng Kim; Heuiseok Lim. A semantic-based video scene segmentation using a deep neural network. Journal of Information Science 2018, 45, 833 -844.
AMA StyleHyesung Ji, Danial Hooshyar, Kuekyeng Kim, Heuiseok Lim. A semantic-based video scene segmentation using a deep neural network. Journal of Information Science. 2018; 45 (6):833-844.
Chicago/Turabian StyleHyesung Ji; Danial Hooshyar; Kuekyeng Kim; Heuiseok Lim. 2018. "A semantic-based video scene segmentation using a deep neural network." Journal of Information Science 45, no. 6: 833-844.
The adaption of user interface (UI) promises to greatly enhance user experience (UX). This is more evident when we focus on elderly people. However, to date there has yet to be any intelligent, domain independent UI/UX system that caters to such elderly users. In this paper we seek to address this gap, and put forward an intelligent UI/UX system, called SmartSenior, that makes use of semi-supervised learning to execute automatic adaptations that would help elderly users by taking into account both their behavioral data and cognitive responses. SmartSenior initially assesses a user’s cognitive capacity and produces a first profile by way of a clustering and classification algorithm. It subsequently produces a personalized UI/UX by altering the profile according to the data on the user’s actions. We assess the efficacy of our system by way of an assessment in which elderly users employed the SmartSenior for 8 weeks. This experiment produced results that were, on the whole, satisfactory.
Heuiseok Lim; Danial Hooshyar; Hyesung Ji; Seolhwa Lee; JaeChoon Jo. SmartSenior: Automatic Content Personalization Through Semi-supervised Learning. Wireless Personal Communications 2018, 105, 461 -473.
AMA StyleHeuiseok Lim, Danial Hooshyar, Hyesung Ji, Seolhwa Lee, JaeChoon Jo. SmartSenior: Automatic Content Personalization Through Semi-supervised Learning. Wireless Personal Communications. 2018; 105 (2):461-473.
Chicago/Turabian StyleHeuiseok Lim; Danial Hooshyar; Hyesung Ji; Seolhwa Lee; JaeChoon Jo. 2018. "SmartSenior: Automatic Content Personalization Through Semi-supervised Learning." Wireless Personal Communications 105, no. 2: 461-473.
In the study of collaborative filtering, scholars and professionals alike have given much attention to user responses of the “one-class” type, feedback like online transactions or “likes”. Such behavior gauges have been integral to many ambient intelligent and context-aware recommendation systems, in which users are furnished with personalized lists of items according to their exhibited tastes. These one-class data, earlier studies have shown, are readily grasped by Bayesian personalized ranking, a pairwise preference assumption. Nevertheless, these works fail to make sufficient use of item similarity models using group preference. To improve performance, we argue in this paper, it is necessary to develop a model that yokes a User preference model to the Group Preference-based Similarity models (called UGPS). UCPG will produce a greater depth of interactions, we argue, because it takes on an entire set of items as opposed to the solitary item used previously. Moreover, a number of clustering methods have been put to work in group preference-based recommendation systems, but there is no consensus as to which offers superior accuracy. To gain clarity, we first built up a pair of versions of UGPS in order to assess the recommendation performances of different approaches to group generation: UGPS-1, which employed K-means, and UGPS-2, using K-NN—according to how efficiently they group their output. This comparison revealed that UGPS-1 tended to improve its recommendation performance as the number of groups and representative item sets grew. In contrast, UGPS-2 exhibited the opposite effect: recommendation performance declined as the number of groups and representative item sets expanded. Lastly, we consider how our UGPS system works with various sophisticated approaches on four real datasets, and demonstrate that UGPS produces more accurate recommendations.
YeongWook Yang; Danial Hooshyar; JaeChoon Jo; Heuiseok Lim. A group preference-based item similarity model: comparison of clustering techniques in ambient and context-aware recommender systems. Journal of Ambient Intelligence and Humanized Computing 2018, 11, 1441 -1449.
AMA StyleYeongWook Yang, Danial Hooshyar, JaeChoon Jo, Heuiseok Lim. A group preference-based item similarity model: comparison of clustering techniques in ambient and context-aware recommender systems. Journal of Ambient Intelligence and Humanized Computing. 2018; 11 (4):1441-1449.
Chicago/Turabian StyleYeongWook Yang; Danial Hooshyar; JaeChoon Jo; Heuiseok Lim. 2018. "A group preference-based item similarity model: comparison of clustering techniques in ambient and context-aware recommender systems." Journal of Ambient Intelligence and Humanized Computing 11, no. 4: 1441-1449.
A mixed-initiative interface conjoins aspects of both adaptable and adaptive interfaces in cases where adaptive customization assistance is added to an adaptable interface, improving the efficacy of customization, efficiency of interactions and user satisfaction. Although many studies showed the efficiency of adaptive customization support, they were either conducted within a laboratory with short-term settings or failed to consider the long-term results of the approach on the elderly. Thus, this study aims to assess the capabilities of adaptive assistance derived from the cognitive and behavioral information of users within an adaptive mixed-initiative UI/UX system (SmartSenior) designed to assist elderly people by improving their familiarity with smart devices. Drawing on cognitive and behavioral data of users, adaptive support was offered by way of customization suggestions that users could accept or disregard at their own discretion. For 10 weeks, 20 senior citizens used SmartSenior, and their actions within the interface were recorded. Half of the test subjects received support and the other half did not. The customization behavior and activity of the two groups were then compared, along with subjective responses concerning the customization support. Results demonstrated that test subjects who were supported made more effective use of SmartSenior’s customization features than those who went unsupported. Among the experimental group, subjects accepted most of the customization suggestions provided, and all of them praised the utility of the support and perceived it as beneficial. Moreover, the results show that customization support is more beneficial to users who would never customize of their own volition; such users will be increasingly likely to do so with support. In conclusion, adaptive customization support helps the elderly to more effectively customize their interface, and hence it would helpfully augment the standard adaptable UI/UX.
Danial Hooshyar; Seolhwa Lee; YeongWook Yang; JaeChoon Jo; Heuiseok Lim. Long-term effects of adaptive customization support on elderly people. Cognition, Technology & Work 2018, 21, 371 -382.
AMA StyleDanial Hooshyar, Seolhwa Lee, YeongWook Yang, JaeChoon Jo, Heuiseok Lim. Long-term effects of adaptive customization support on elderly people. Cognition, Technology & Work. 2018; 21 (3):371-382.
Chicago/Turabian StyleDanial Hooshyar; Seolhwa Lee; YeongWook Yang; JaeChoon Jo; Heuiseok Lim. 2018. "Long-term effects of adaptive customization support on elderly people." Cognition, Technology & Work 21, no. 3: 371-382.
Although game‐based learning has been increasingly promoted in education, there is a need to adapt game content to individual needs for personalized learning. Procedural content generation (PCG) offers a solution for difficulty in developing game contents automatically by algorithmic means as it can generate individually customizable game contents applicable to various objectives. In this paper, we advanced a data‐driven PCG approach benefiting from a genetic algorithm and support vector machines to automatically generate educational‐game contents tailored to individuals' abilities. In contrast to other content generation approaches, the proposed method is not dependent on designer's intuition in applying game contents to fit a player's abilities. We assessed this data‐driven PCG approach at length and showed its effectiveness by conducting an empirical study of children who played an educational language‐learning game to cultivate early English‐reading skills. To affirm the efficacy of our proposed method, we evaluated the data‐driven approach against a heuristic‐based approach. Our results clearly demonstrated two things. First, users realized greater performance gains from playing contents tailored to their abilities compared with playing uncustomized game contents. Second, this data‐driven approach was more effective in generating contents closely matching a specific player‐performance target than the heuristic‐based approach.
D. Hooshyar; M. Yousefi; M. Wang; H. Lim. A data-driven procedural-content-generation approach for educational games. Journal of Computer Assisted Learning 2018, 34, 731 -739.
AMA StyleD. Hooshyar, M. Yousefi, M. Wang, H. Lim. A data-driven procedural-content-generation approach for educational games. Journal of Computer Assisted Learning. 2018; 34 (6):731-739.
Chicago/Turabian StyleD. Hooshyar; M. Yousefi; M. Wang; H. Lim. 2018. "A data-driven procedural-content-generation approach for educational games." Journal of Computer Assisted Learning 34, no. 6: 731-739.
Modeling and predicting player behavior is of the utmost importance in developing games. Experience has proven that, while theory-driven approaches are able to comprehend and justify a model's choices, such models frequently fail to encompass necessary features because of a lack of insight of the model builders. In contrast, data-driven approaches rely much less on expertise, and thus offer certain potential advantages. Hence, this study conducts a systematic review of the extant research on data-driven approaches to game player modeling. To this end, we have assessed experimental studies of such approaches over a nine-year period, from 2008 to 2016; this survey yielded 46 research studies of significance. We found that these studies pertained to three main areas of focus concerning the uses of data-driven approaches in game player modeling. One research area involved the objectives of data-driven approaches in game player modeling: behavior modeling and goal recognition. Another concerned methods: classification, clustering, regression, and evolutionary algorithm. The third was comprised of the current challenges and promising research directions for data-driven approaches in game player modeling.
Danial Hooshyar; Moslem Yousefi; Heuiseok Lim. Data-Driven Approaches to Game Player Modeling. ACM Computing Surveys 2018, 50, 1 -19.
AMA StyleDanial Hooshyar, Moslem Yousefi, Heuiseok Lim. Data-Driven Approaches to Game Player Modeling. ACM Computing Surveys. 2018; 50 (6):1-19.
Chicago/Turabian StyleDanial Hooshyar; Moslem Yousefi; Heuiseok Lim. 2018. "Data-Driven Approaches to Game Player Modeling." ACM Computing Surveys 50, no. 6: 1-19.
Recent years have seen growing interest in open-ended interactive educational tools such as games. One of the most crucial aspects of developing games lies in modeling and predicting individual behavior, the study of computational models of players in games. Although model-based approaches have been considered standard for this purpose, their application is often extremely difficult due to the huge space of actions that can be created by educational games. For this reason, data-driven approaches have shown promise, in part because they are not completely reliant on expert knowledge. This study seeks to systematically review the existing research on the use of data-driven approaches in player modeling of educational games. The primary objectives of this study are to identify, classify, and bring together the relevant approaches. We have carefully surveyed a 10-year sample (2008–2017) of research studies conducted on data-driven approaches in player modeling of educational games, and thereby found 67 significant research works. However, our criteria for inclusion reduced the sample to 21 studies that addressed four primary research questions, and so we analyzed and classified the questions, methods, and findings of these published works, which we evaluated and from which we drew conclusions based on non-statistical methods. We found that there are three primary avenues along which data-driven approaches have been studied in educational games research: first, the objective of data-driven approaches in player modeling of educational games, namely behavior modeling, goal recognition, and procedural content generation; second, approaches employed in such modeling; finally, current challenges of using data-driven approaches in player modeling of educational games, namely game data, temporal forecasting in player models, statistical techniques, algorithmic efficiency, knowledge engineering, problem of generalizability, and data sparsity problem. In conclusion we addressed four critical future challenges in the area, namely, the lack of proper and rich data publicly available to the researchers, the lack of a data-driven method to identify conceptual features from log data, hybrid player modeling approaches, and data mining techniques for individual prediction.
Danial Hooshyar; Moslem Yousefi; Heuiseok Lim. A systematic review of data-driven approaches in player modeling of educational games. Artificial Intelligence Review 2017, 52, 1997 -2017.
AMA StyleDanial Hooshyar, Moslem Yousefi, Heuiseok Lim. A systematic review of data-driven approaches in player modeling of educational games. Artificial Intelligence Review. 2017; 52 (3):1997-2017.
Chicago/Turabian StyleDanial Hooshyar; Moslem Yousefi; Heuiseok Lim. 2017. "A systematic review of data-driven approaches in player modeling of educational games." Artificial Intelligence Review 52, no. 3: 1997-2017.
Nearly 30% of the input energy to a diesel engine is wasted through the exhaust gas; thus, considerable attention has been directed toward developing efficient heat recovery systems for these engines. Given the demonstrated ability of nanofluids to boost the heat transfer rate of heat exchangers, these heat transfer fluids merit consideration for use in diesel exhaust heat recovery systems. In this study, the effects of employing nanofluids on the optimum design of these systems are investigated. An existing heat diesel engine exhaust heat recovery system is modeled to work with Al2O3/water and a modified imperialist competitive algorithm is employed for the optimization. Seven variables consisting of five heat exchanger geometric characteristics together with nanoparticle volume fraction and coolant mass flow rate are considered as design variables. The heat exchanger cost and charging rate of the storage tank are optimization objectives, while the greenhouse gas savings of the heat recovery system are assessed for measuring the environmental impact of the energy recovery. The results indicate that the proposed approach can overcome the challenge of finding the near-optimal design of this complex system and using nanofluids enhances the performance of the heat recovery heat exchanger.
Moslem Yousefi; Danial Hooshyar; Joong H Kim; Marc A Rosen; Heuiseok Lim. Optimum waste heat recovery from diesel engines: Thermo-economic assessment of nanofluid-based systems using a robust evolutionary approach. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 2017, 233, 65 -82.
AMA StyleMoslem Yousefi, Danial Hooshyar, Joong H Kim, Marc A Rosen, Heuiseok Lim. Optimum waste heat recovery from diesel engines: Thermo-economic assessment of nanofluid-based systems using a robust evolutionary approach. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. 2017; 233 (1):65-82.
Chicago/Turabian StyleMoslem Yousefi; Danial Hooshyar; Joong H Kim; Marc A Rosen; Heuiseok Lim. 2017. "Optimum waste heat recovery from diesel engines: Thermo-economic assessment of nanofluid-based systems using a robust evolutionary approach." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 233, no. 1: 65-82.