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Journal articles on the topic 'Learning objects'

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Published: 4 June 2021
Last updated: 31 January 2023

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1

Dodani, Mahesh H. "The Dark Side of Object Learning: Learning Objects." Journal of Object Technology 1, no. 5 (2002): 37. http://dx.doi.org/10.5381/jot.2002.1.5.c3.

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2

Saleh, Mostafa S. "Building Interoperable Learning Objects Using Reduced Learning Object Metadata." E-Learning and Digital Media 2, no. 3 (September 2005): 299–313. http://dx.doi.org/10.2304/elea.2005.2.3.9.

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The new e-learning generation depends on Semantic Web technology to produce learning objects. As the production of these components is very costly, they should be produced and registered once, and reused and adapted in the same context or in other contexts as often as possible. To produce those components, developers should use learning standards to describe these objects in order to support interoperability. IEEE Learning Object Metadata (LOM) is the most dominant standard for describing learning objects, in which 76 different elements are used to describe the different aspects of e-learning. Nonetheless, it will still be time consuming to build these learning objects. This paper introduces a model for building Global Interoperable Learning Objects (GILO) for the e-learning community. This is achieved by using a reduced set of the LOM elements, and giving a unique global ID to the learning object. This will enable software agents to query these learning object repositories, to automatically deliver the required material to the e-learning consumer.
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3

Vetromille-Castro, Rafael, Anne Marie Moor, Gabriela Bohlmann Duarte, and Nairana Hoffmann Sedrez. "From Learning Objects to Language Learning Objects." International Journal of Computer-Assisted Language Learning and Teaching 3, no. 2 (April 2013): 82–96. http://dx.doi.org/10.4018/ijcallt.2013040105.

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Learning Objects (LOs) have increasingly become of interest to users and researchers of Information and Communication Technologies (Wiley, 2002; Gibson, 2002; Leffa, 2006). There are several definitions, an ample discussion and criticism in relation to what can be considered a LO. Leffa (2006) indicates the state of the art of LOs and points to the lack of theoretical support in the production of such resources. Therefore, since more attention has been paid to technological aspects than to pedagogical ones in the development of LOs, the authors consider it necessary to have a theoretical basis that supports the design of such objects and makes them congruent to the learning of foreign languages (FL) with an emphasis on communication. Hence, this paper proposes a definition of Language Learning Objects (LLOs) that attend to the principles of Communicative Language Teaching (Canale & Swain, 1980; Ellis, 1999, 2005; Paiva, 2009) and Pedagogical and Design Usability (Vetromille-Castro, 2003).
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4

Clarke, Alex, Philip J. Pell, Charan Ranganath, and Lorraine K. Tyler. "Learning Warps Object Representations in the Ventral Temporal Cortex." Journal of Cognitive Neuroscience 28, no. 7 (July 2016): 1010–23. http://dx.doi.org/10.1162/jocn_a_00951.

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The human ventral temporal cortex (VTC) plays a critical role in object recognition. Although it is well established that visual experience shapes VTC object representations, the impact of semantic and contextual learning is unclear. In this study, we tracked changes in representations of novel visual objects that emerged after learning meaningful information about each object. Over multiple training sessions, participants learned to associate semantic features (e.g., “made of wood,” “floats”) and spatial contextual associations (e.g., “found in gardens”) with novel objects. fMRI was used to examine VTC activity for objects before and after learning. Multivariate pattern similarity analyses revealed that, after learning, VTC activity patterns carried information about the learned contextual associations of the objects, such that objects with contextual associations exhibited higher pattern similarity after learning. Furthermore, these learning-induced increases in pattern information about contextual associations were correlated with reductions in pattern information about the object's visual features. In a second experiment, we validated that these contextual effects translated to real-life objects. Our findings demonstrate that visual object representations in VTC are shaped by the knowledge we have about objects and show that object representations can flexibly adapt as a consequence of learning with the changes related to the specific kind of newly acquired information.
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5

Dunning, Jeremy, Kellie Donoghue, Abtar Kaur, and David Daniels. "Re-Purposeable Learning Objects Based on Teaching and Learning Styles." International Journal of Wireless Networks and Broadband Technologies 2, no. 4 (October 2012): 1–11. http://dx.doi.org/10.4018/ijwnbt.2012100101.

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The interactive, multimedia learning object has become an important part of high quality online education. The cost of producing such learning objects can be prohibitive. Re-purposeable learning objects made with learning object templates allow instructors with little or no programming experience to produce highly interactive and immersive learning objects. These learning object templates are based on key styles of teaching and learning and can be used to create and customize new learning objects within those styles, without creating new source code. The templates allow instructors to create learning objects simply by inserting text, and media (images, movies, etc.) because they closely mimic specific teaching strategies.
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6

Markovic, Suzana. "E-learning objects: Knowledge objects." Bizinfo Blace 6, no. 1 (2015): 35–42. http://dx.doi.org/10.5937/bizinfo1501035m.

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7

Assis, Luciana, Ana Carolina Rodrigues, Alessandro Vivas, Cristiano Grijó Pitangui, Cristiano Maciel Silva, and Fabiano Azevedo Dorça. "Relationship Between Learning Styles and Learning Objects." International Journal of Distance Education Technologies 20, no. 1 (January 2022): 1–18. http://dx.doi.org/10.4018/ijdet.296698.

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The automation of learning object recommendation and learning styles detection processes has attracted the interest of many researchers. Some works consider Learning Styles to recommend Learning Objects. On the other hand, other works automatically detect Learning Styles, analyzing the behavior of students in Intelligent Tutorial Systems in relation to the use of Learning Objects. Taking into account that advances in this field of research have been constantly presented in recent years, this paper analyzes the results of works discovered through a Systematic Literature Review. The main objective was to discover and document the relationships between Learning Styles and Learning Objects considered by researchers, in order to provide accurate content recommendations. The results show inconsistencies in the process, indicating that more and more in-depth research is still needed to allow a more accurate understanding of how to consider Learning Styles in the Learning Object recommendation process.
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8

Plessis, Jacques Du, and Alex Koohang. "Securing learning in learning objects." International Journal of Innovation and Learning 4, no. 2 (2007): 197. http://dx.doi.org/10.1504/ijil.2007.011694.

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9

Abad, Cristina L. "Learning through creating learning objects." ACM SIGCSE Bulletin 40, no. 3 (August 25, 2008): 255–59. http://dx.doi.org/10.1145/1597849.1384340.

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10

Smith, Linda B. "Learning to Recognize Objects." Psychological Science 14, no. 3 (May 2003): 244–50. http://dx.doi.org/10.1111/1467-9280.03439.

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A theory of object recognition requires a theory of shape. Despite considerable empirical and theoretical research, however, a definition of object shape has proved elusive. Two experiments provide new insights by showing that children's object recognition changes dramatically during the period between 17 and 25 months. During this time, children develop the ability to recognize stylized three-dimensional caricatures of known and novel objects. This ability is linked to the number of object names in children's vocabularies, suggesting that category learning may be a driving force behind the developmental changes.
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11

Azambuja Silveira, Ricardo, Rafaela Lunardi Comarella, Ronaldo Lima Rocha Campos, Jonas Vian, and Fernando De La Prieta. "Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning Objects." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 4, no. 4 (December 24, 2015): 69–82. http://dx.doi.org/10.14201/adcaij2015446982.

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This paper discusses some important issues regarding the the management of Learning objects covering searching over repositories and different approaches of recommendation systems and presents a multiagent system based application model for indexing, retrieving and recommending learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata (data about data) standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object and improve the relevance of the results we propose an information retrieval model based on a multiagent system approach and an ontological model to describe the covered knowledge domain.
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12

Were, Graeme. "Objects of Learning." Journal of Material Culture 8, no. 1 (March 2003): 25–44. http://dx.doi.org/10.1177/1359183503008001761.

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13

Walsh, Kieran. "Reusable learning objects." BMJ 332, no. 7551 (May 18, 2006): 1193.2. http://dx.doi.org/10.1136/bmj.332.7551.1193-a.

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14

Vinha, Antonio. "Reusable learning objects." ACM SIGCSE Bulletin 37, no. 3 (September 2005): 413. http://dx.doi.org/10.1145/1151954.1067617.

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15

Holmes, John. "Online Learning Objects." Public Services Quarterly 1, no. 4 (September 2003): 1–9. http://dx.doi.org/10.1300/j295v01n04_01.

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16

Knolmayer, Gerhard F. "E-Learning Objects." Wirtschaftsinformatik 46, no. 3 (June 2004): 222–24. http://dx.doi.org/10.1007/bf03250940.

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17

Allen, Claudine A., and Ezra K. Mugisa. "Improving Learning Object Reuse Through OOD: A Theory of Learning Objects." Journal of Object Technology 9, no. 6 (2010): 51. http://dx.doi.org/10.5381/jot.2010.9.6.a3.

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18

Schultz, Lainie. "Object-based learning, or learning from objects in the anthropology museum." Review of Education, Pedagogy, and Cultural Studies 40, no. 4 (August 8, 2018): 282–304. http://dx.doi.org/10.1080/10714413.2018.1532748.

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19

Li, Xiangyang, Shuqiang Jiang, and Jungong Han. "Learning Object Context for Dense Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8650–57. http://dx.doi.org/10.1609/aaai.v33i01.33018650.

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Dense captioning is a challenging task which not only detects visual elements in images but also generates natural language sentences to describe them. Previous approaches do not leverage object information in images for this task. However, objects provide valuable cues to help predict the locations of caption regions as caption regions often highly overlap with objects (i.e. caption regions are usually parts of objects or combinations of them). Meanwhile, objects also provide important information for describing a target caption region as the corresponding description not only depicts its properties, but also involves its interactions with objects in the image. In this work, we propose a novel scheme with an object context encoding Long Short-Term Memory (LSTM) network to automatically learn complementary object context for each caption region, transferring knowledge from objects to caption regions. All contextual objects are arranged as a sequence and progressively fed into the context encoding module to obtain context features. Then both the learned object context features and region features are used to predict the bounding box offsets and generate the descriptions. The context learning procedure is in conjunction with the optimization of both location prediction and caption generation, thus enabling the object context encoding LSTM to capture and aggregate useful object context. Experiments on benchmark datasets demonstrate the superiority of our proposed approach over the state-of-the-art methods.
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20

You, Yeong Mahn. "How Could We Marry Knowledge Management to e-Learning with Learning Objects?; The Possicilitiey and Limitations." Journal of Educational Technology 17, no. 2 (June 30, 2001): 53–89. http://dx.doi.org/10.17232/kset.17.2.53.

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21

Alonso, Fernando, Genoveva López, Daniel Manrique, and José María Viñes. "Learning objects, learning objectives and learning design." Innovations in Education and Teaching International 45, no. 4 (November 2008): 389–400. http://dx.doi.org/10.1080/14703290802377265.

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22

Sheng-Hung, Chung. "Course delivery and module learning via learning objects (knowledge map) in mobile learning environment." Asian Association of Open Universities Journal 7, no. 1 (September 1, 2012): 43–54. http://dx.doi.org/10.1108/aaouj-07-01-2012-b004.

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This paper focuses on the integration of the learning objects and knowledge map as the learning sequence suggestion in the mobile learning environment and explains the technologies involved, the applications and the issues of usability, accessibility, evaluation and effectiveness. Mobile learning has open up new path for learning support and opportunities to reach wider audience (learner) for education. This research focuses on using the knowledge map to store the characteristics of each learning object via concept schemas and represent the corresponding learning accessibility in the mobile learning environment. The proposed architecture provides a medium for the learning accessibility of learners through mobile applications and wireless portable devices such as smart phones, PDAs and tablet PCs. The approach using the combination of "touch" and "observe" spatial learning objects provides an intelligent solution to creating, sharing and improving the efficiency of mobile learning. The proposed mobile learning environment architecture consists of knowledge map components mainly, navigation, concept schemas and learning object path. By using these knowledge structures, this study may enhance and enrich the concept and activity of adaptive learning in different individuals and communities. The spatial knowledge map constructed was useful in identifying the characteristics of the learning objects (e.g., learning object 1: lesson with navigating sentences, learning object 2: lesson with navigating sentence and code explanation, etc) and automatically matches the most appropriate learning contentand path suitable for learners. The architecture of the platform discussed in this study using the learning objects approach and knowledge map would facilitate a more widespread use of mobile learning, including courses or modules delivery of individualised learning path and learning style analysis.
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23

Wang, Chao, Xuehe Zhang, Xizhe Zang, Yubin Liu, Guanwen Ding, Wenxin Yin, and Jie Zhao. "Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review." Sensors 20, no. 13 (July 2, 2020): 3707. http://dx.doi.org/10.3390/s20133707.

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As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.
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24

Smith Nash, Susan. "Learning Objects, Learning Object Repositories, and Learning Theory: Preliminary Best Practices for Online Courses." Interdisciplinary Journal of e-Skills and Lifelong Learning 1 (2005): 217–28. http://dx.doi.org/10.28945/422.

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25

Sabitha, Sai, Deepti Mehrotra, and Abhay Bansal. "Enhanced Learning by Extending Metadata of Learning Objects with Knowledge Objects." International Journal of Education and Learning 3, no. 1 (March 31, 2014): 1–12. http://dx.doi.org/10.14257/ijel.2014.3.1.01.

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26

Del Moral-Pérez, Esther, Ana Cernea, and Lourdes Villalustre. "Connectivist Learning Objects and Learning Styles." Interdisciplinary Journal of e-Skills and Lifelong Learning 9 (2013): 105–24. http://dx.doi.org/10.28945/1866.

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27

Kay, Robin H., and Liesel Knaack. "Evaluating the learning in learning objects." Open Learning: The Journal of Open, Distance and e-Learning 22, no. 1 (February 2007): 5–28. http://dx.doi.org/10.1080/02680510601100135.

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28

Markgraf, Jill. "From Learning Communities to Learning Objects." Journal of Library Administration 45, no. 3-4 (November 21, 2006): 559. http://dx.doi.org/10.1300/j111v45n03_20.

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29

Rey Lopez, Marta, Rebeca P. Diaz Redondo, Ana Fernandez Vilas, Jose J. Pazos Arias, and Martin Lopez Nores. "Adaptive Learning Objects for t-learning." IEEE Latin America Transactions 5, no. 6 (October 2007): 401–8. http://dx.doi.org/10.1109/tla.2007.4395228.

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30

Garcia Gonzalez, Luisa Aleyda, and Wilson Vicente Ruggiero. "Collaborative e-learning and Learning Objects." IEEE Latin America Transactions 7, no. 5 (September 2009): 569–77. http://dx.doi.org/10.1109/tla.2009.5361195.

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31

Chang, Hsiu-Ju. "Beyond the Representation: Cognition in Manipulative Learning Objects within Learning Simple Equations." International Journal of Information and Education Technology 5, no. 11 (2015): 855–59. http://dx.doi.org/10.7763/ijiet.2015.v5.626.

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32

Ke, Chih-Kun, Kai-Ping Liu, and Wen-Chin Chen. "Building a Smart E-Portfolio Platform for Optimal E-Learning Objects Acquisition." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/896027.

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In modern education, an e-portfolio platform helps students in acquiring e-learning objects in a learning activity. Quality is an important consideration in evaluating the desirable e-learning object. Finding a means of determining a high quality e-learning object from a large number of candidate e-learning objects is an important requirement. To assist student learning in a modern e-portfolio platform, this work proposed an optimal selection approach determining a reasonable e-learning object from various candidate e-learning objects. An optimal selection approach which uses advanced information techniques is proposed. Each e-learning object undergoes a formalization process. An Information Retrieval (IR) technique extracts and analyses key concepts from the student’s previous learning contexts. A context-based utility model computes the expected utility values of various e-learning objects based on the extracted key concepts. The expected utility values of e-learning objects are used in a multicriteria decision analysis to determine the optimal selection order of the candidate e-learning objects. The main contribution of this work is the demonstration of an effective e-learning object selection method which is easy to implement within an e-portfolio platform and which makes it smarter.
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33

Roth, Dan, Ming-Hsuan Yang, and Narendra Ahuja. "Learning to Recognize Three-Dimensional Objects." Neural Computation 14, no. 5 (May 1, 2002): 1071–103. http://dx.doi.org/10.1162/089976602753633394.

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A learning account for the problem of object recognition is developed within the probably approximately correct (PAC) model of learnability. The key assumption underlying this work is that objects can be recognized (or discriminated) using simple representations in terms of syntactically simple relations over the raw image. Although the potential number of these simple relations could be huge, only a few of them are actually present in each observed image, and a fairly small number of those observed are relevant to discriminating an object. We show that these properties can be exploited to yield an efficient learning approach in terms of sample and computational complexity within the PAC model. No assumptions are needed on the distribution of the observed objects, and the learning performance is quantified relative to its experience. Most important, the success of learning an object representation is naturally tied to the ability to represent it as a function of some intermediate representations extracted from the image. We evaluate this approach in a large-scale experimental study in which the SNoW learning architecture is used to learn representations for the 100 objects in the Columbia Object Image Library. Experimental results exhibit good generalization and robustness properties of the SNoW-based method relative to other approaches. SNoW's recognition rate degrades more gracefully when the training data contains fewer views, and it shows similar behavior in some preliminary experiments with partially occluded objects.
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34

Williams, Christopher K. I., and Michalis K. Titsias. "Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning." Neural Computation 16, no. 5 (May 1, 2004): 1039–62. http://dx.doi.org/10.1162/089976604773135096.

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We consider data that are images containing views of multiple objects. Our task is to learn about each of the objects present in the images. This task can be approached as a factorial learning problem, where each image must be explained by instantiating a model for each of the objects present with the correct instantiation parameters. A major problem with learning a factorial model is that as the number of objects increases, there is a combinatorial explosion of the number of configurations that need to be considered. We develop a method to extract object models sequentially from the data by making use of a robust statistical method, thus avoiding the combinatorial explosion, and present results showing successful extraction of objects from real images.
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35

Agaba, Joab E., and Jude T. Lubega. "Adaptation of Learning Objects with Multi-Format Assets." International Journal of Information and Education Technology 6, no. 1 (2016): 76–79. http://dx.doi.org/10.7763/ijiet.2016.v6.662.

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36

Kruglov, Artem V. "The Unsupervised Learning Algorithm for Detecting Ellipsoid Objects." International Journal of Machine Learning and Computing 9, no. 3 (June 2019): 255–60. http://dx.doi.org/10.18178/ijmlc.2019.9.3.795.

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37

Weinreich, Donna M., and Catherine J. Tompkins. "Learning Objects and Gerontology." Educational Gerontology 32, no. 9 (October 2006): 785–99. http://dx.doi.org/10.1080/03601270600850925.

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38

Wallis, Guy, and Heinrich Bülthoff. "Learning to recognize objects." Trends in Cognitive Sciences 3, no. 1 (January 1999): 22–31. http://dx.doi.org/10.1016/s1364-6613(98)01261-3.

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39

Bischof, Walter. "Learning to recognize objects." Spatial Vision 13, no. 2-3 (2000): 297–304. http://dx.doi.org/10.1163/156856800741117.

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40

Ordonez, Vicente, Wei Liu, Jia Deng, Yejin Choi, Alexander C. Berg, and Tamara L. Berg. "Learning to name objects." Communications of the ACM 59, no. 3 (February 25, 2016): 108–15. http://dx.doi.org/10.1145/2885252.

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41

Billings, Diane M. "Using Reusable Learning Objects." Journal of Continuing Education in Nursing 41, no. 2 (February 1, 2010): 54–55. http://dx.doi.org/10.3928/00220124-20100126-08.

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42

Barritt, Chuck. "Learning objects & ISD." Performance Improvement 41, no. 7 (August 2002): 30–34. http://dx.doi.org/10.1002/pfi.4140410707.

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43

Woods, K., D. Cook, L. Hall, K. Bowyer, and L. Stark. "Learning Membership Functions in a Function-Based Object Recognition System." Journal of Artificial Intelligence Research 3 (October 1, 1995): 187–222. http://dx.doi.org/10.1613/jair.236.

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Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. Such systems naturally associate a ``measure of goodness'' or ``membership value'' with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value when evaluating a primitive of a particular physical property of an object. In previous versions of a recognition system known as Gruff, the membership function for each of the primitive evaluations was hand-crafted by the system designer. In this paper, we provide a learning component for the Gruff system, called Omlet, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally applicable to any problem in which low-level membership values are combined through an and-or tree structure to give a final overall membership value.
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44

Smith, Cybelle M., and Kara D. Federmeier. "Neural Signatures of Learning Novel Object–Scene Associations." Journal of Cognitive Neuroscience 32, no. 5 (May 2020): 783–803. http://dx.doi.org/10.1162/jocn_a_01530.

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Objects are perceived within rich visual contexts, and statistical associations may be exploited to facilitate their rapid recognition. Recent work using natural scene–object associations suggests that scenes can prime the visual form of associated objects, but it remains unknown whether this relies on an extended learning process. We asked participants to learn categorically structured associations between novel objects and scenes in a paired associate memory task while ERPs were recorded. In the test phase, scenes were first presented (2500 msec), followed by objects that matched or mismatched the scene; degree of contextual mismatch was manipulated along visual and categorical dimensions. Matching objects elicited a reduced N300 response, suggesting visuostructural priming based on recently formed associations. Amplitude of an extended positivity (onset ∼200 msec) was sensitive to visual distance between the presented object and the contextually associated target object, most likely indexing visual template matching. Results suggest recent associative memories may be rapidly recruited to facilitate object recognition in a top–down fashion, with clinical implications for populations with impairments in hippocampal-dependent memory and executive function.
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45

Santos, Paulo, Chris Needham, and Derek Magee. "Inductive learning spatial attention." Sba: Controle & Automação Sociedade Brasileira de Automatica 19, no. 3 (September 2008): 316–26. http://dx.doi.org/10.1590/s0103-17592008000300007.

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This paper investigates the automatic induction of spatial attention from the visual observation of objects manipulated on a table top. In this work, space is represented in terms of a novel observer-object relative reference system, named Local Cardinal System, defined upon the local neighbourhood of objects on the table. We present results of applying the proposed methodology on five distinct scenarios involving the construction of spatial patterns of coloured blocks.
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46

Perera, Ian, and James Allen. "SALL-E: Situated Agent for Language Learning." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 29, 2013): 1241–47. http://dx.doi.org/10.1609/aaai.v27i1.8475.

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We describe ongoing research towards building a cognitively plausible system for near one-shot learning of the meanings of attribute words and object names, by grounding them in a sensory model. The system learns incrementally from human demonstrations recorded with the Microsoft Kinect, in which the demonstrator can use unrestricted natural language descriptions. We achieve near-one shot learning of simple objects and attributes by focusing solely on examples where the learning agent is confident, ignoring the rest of the data. We evaluate the system's learning ability by having it generate descriptions of presented objects, including objects it has never seen before, and comparing the system response against collected human descriptions of the same objects. We propose that our method of retrieving object examples with a k-nearest neighbor classifier using Mahalanobis distance corresponds to a cognitively plausible representation of objects. Our initial results show promise for achieving rapid, near one-shot, incremental learning of word meanings.
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47

Rodríguez, Verónica, and Gerardo Ayala. "Design Methodology for Adaptivity and Adaptability of Learning Object’s Interface." International Journal of Online Pedagogy and Course Design 3, no. 2 (April 2013): 77–95. http://dx.doi.org/10.4018/ijopcd.2013040105.

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In this paper the authors introduce a Design Methodology for Adaptivity and Adaptability of Learning Object’s Interface. The interface should be seen as the action space where mediatic objects are presented for user interaction. A learning object developed based on this methodology adapts itself to the user; it is not the user who must adapt her/himself to the learning object. This adaptation implies the design of the learning object interface, which includes the processes and structures for adaptivity and adaptability. The paper includes a discussion of methodology, the architecture and its components.
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48

UDE, ALEŠ, DAMIR OMRČEN, and GORDON CHENG. "MAKING OBJECT LEARNING AND RECOGNITION AN ACTIVE PROCESS." International Journal of Humanoid Robotics 05, no. 02 (June 2008): 267–86. http://dx.doi.org/10.1142/s0219843608001406.

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The exploration and learning of new objects is an essential capability of a cognitive robot. In this paper we focus on making use of the robot's manipulation abilities to learn complete object representations suitable for 3D object recognition. Taking control of the object allows the robot to focus on relevant parts of the images, thus bypassing potential pitfalls of purely bottom-up attention and segmentation. The main contribution of the paper consists in integrated visuomotor processes that allow the robot to learn object representations by manipulation without having any prior knowledge about the objects. Our experimental results show that the acquired data is of sufficient quality to train a classifier that can recognize 3D objects independently of the viewpoint.
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49

Notargiacomo Mustaro, Pollyana, and Ismar Frango Silveira. "Learning Objects: Adaptive Retrieval through Learning Styles." Interdisciplinary Journal of e-Skills and Lifelong Learning 2 (2006): 035–46. http://dx.doi.org/10.28945/619.

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50

Rojas-Contreras, M., and O. Portilla-Jaimes. "Integration of learning objects for adaptative learning." IOP Conference Series: Materials Science and Engineering 519 (May 28, 2019): 012030. http://dx.doi.org/10.1088/1757-899x/519/1/012030.

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