Introduction
Background
Social tagging of learning objects
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LOs are labeled with users’ personal tags, which reflect their personal way of describing, classifying, locating and navigating to LOs. This could offer a personalized way for searching which is delivered by users’ tags and not by an externally defined classification system (Cho et al., 2011; Vuorikari et al., 2010)
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LOs are tagged by different users with an increased amount of tags that reflect “the wisdom of crowds”. This could offer a mechanism to capture users’ contextual value of LOs (e.g., experiences from using the LO in teaching and learning practice), which could be different from creators’ anticipated contextual value (Zervas & Sampson, 2014; Trant, 2009; Dahl & Vossen, 2008).
Related studies: analysis of the Tag vocabulary of social tagging systems
Research method
Data collection and normalization
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LOs tagging: The user can characterize with his/her selected tags any kind (URL or digital file) of science education LO. The tags that the user can add to the science education LOs describe the topic and/or the subject domain of a science education LO related with the science curriculum.
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Guided Tagging: During the tagging process of a science education LO, the user is presented first with his/her tags previously used for characterizing other science education LOs(referred to as personal tags) and then with tags that are most frequently used by other users regarding this specific science education LO (referred to as popular tags).
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Auto-Suggested Tagging: During the tagging process, the user is presented with suggested tags that have been used by other users and are relevant with the tag that the user is typing.
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Creation of user’s personal LOs collection: The user has the capability to save to his/her personal list, science education LOs uploaded by other users and browse the tags that these users have used.
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Browse LOs via tag cloud: The user can search and browse science education LOs using an appropriately formatted tag cloud produced by the tags that all users of the tool have offered. The tags that have been previously used by the user are presented with red color within the tag cloud.
Variables | Value |
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Taggers | 321 |
Tagged Science Education Resources | 2018 |
Social Tags | 11175 |
Distinct Social Tags | 2735 |
Methodology
Tag growth
Tag reuse
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High tag growth and low tag reuse: this means that users are mainly adding new tags and they are not re-using existing tags. As a result, the specificity of tags is increasing and this could facilitate users to narrow their search results when using specific tags.
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Low tag growth and high tag reuse: this means that the social tagging system is highly collaborative and LOs’ tags are increased over time. However, the specificity of tags is decreasing and any single tag references many LOs. In this case, average number of tags used in a search query should be increased by the users in order to narrow the search results.
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High tag growth and high tag reuse: this means that the users are both adding new tags and re-using existing tags. In this case, tag growth and tag reuse should be examined in combination with other metrics (such as tag discrimination that is described below), so as to be able to interpret the behavior of the social tagging system.
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Low tag growth and low tag reuse: this means that for some reason the system is not used at all for tagging by its users.
Tag discrimination
Results and discussion
Analysis of tag growth
New users | New LOs | |
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New Tags |
r = 0.287 |
r = 0.545 |
p < 0.05 |
p < 0.001 |
LO Type | # of Tags | # of LOs | Tag growth |
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Simulation | 615 | 249 | 2.47 |
Video | 333 | 139 | 2.40 |
Lesson plan | 216 | 139 | 1.55 |
Text | 318 | 271 | 1.17 |
Questionnaire | 179 | 167 | 1.07 |
Image | 1074 | 1053 | 1.02 |
Total | 2735 | 2018 |
Analysis of tag reuse
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Period 1 (From May-2010 to May-2012): during this period the system had high tag growth and low tag reuse. This means that the specificity of tags was increasing and this facilitated the navigating to LOs via social tags in the OSR repository.
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Period 2 (From June-2012 to May-2014): during this period the system had low tag growth and low tag reuse. This means that the tagging behavior was on decline by the repository users, which could be related to external factors that had to do with the support of the operation of the OSR repository by its owners.
LO type | tag reuse |
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Simulation | 1.87 |
Video | 1.92 |
Lesson plan | 1.76 |
Text | 1.69 |
Questionnaire | 1.75 |
Image | 1.95 |
Analysis of tag discrimination
LO Type | Tag discrimination |
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Simulation | 3.48 |
Video | 3.69 |
Lesson plan | 3.92 |
Text | 3.31 |
Questionnaire | 3.53 |
Image | 3.24 |
Conclusions and future work
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The growth of the tag vocabulary is strongly correlated with the addition of new LOs in the OSR Repository, whereas the correlation with the registration of new users is weak. These findings can be explained considering the focus of the OSR Repository to Science Education LOs. More specifically, the tag vocabulary is expected to grow significantly as new LOs enter the system and teachers can share their insights and experiences on these new resources. On the contrary, the tag vocabulary is expected to grow to a lesser rate when an increasing number of teachers share their (possibly overlapping) insights and experiences on the same pool of LOs.
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Tag reuse in the OSR Repository is mainly focused to support classification of LOs towards future retrieval. On the other hand, reuse of tags for characterizing different LOs towards facilitating the creation of enhanced LOs descriptions is limited. A possible reason for that could be the tagging interface, which does not highly facilitate tag reuse, since users are presented (during the tagging process) first with their personal tags and then with the popular tags that have been already added by other users.The evolution of tag vocabulary in terms of tag growth was higher for LO types with higher interactivity and semantic density (such as simulations, videos and lesson plans) compared to other LO types with low interactivity and low semantic density (such as texts, questionnaires and images). This means that LOs with higher interactivity and semantic density tended to attract more (distinct) tags from teachers, perhaps due to increased use of such LOs in the everyday practice. On the other hand, no significant differences were identified for the tag reuse and discrimination metrics among the different LO types.
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Overall, the frequency of tag reuse in the OSR Repository is not uniform. More specifically, there are few tags that have been reused many times and many tags that have been reused few times. The same also applies for users, namely there are few users that have re-used many times and many users that have reused few tags. Both distributions of tags per their frequency of reuse occurrence and the users per their frequency of tags reused resemble a power law. This behavior is fully aligned with the behavior of other social tagging systems applied in repositories beyond LORs.
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LORs administrators could monitor the tag growth metric, so as to be able to understand when the tag vocabulary matures and could be used to supplement and/or complete the existing ‘official’ classification system (such as the IEEE LOM standard) of a LOR. Moreover, by monitoring the entropy of tag vocabulary, as well as the tag reuse and tag discrimination metrics, LORs administrators can understand the tagging behavior of the users of the LOR. These metrics could also be used as a means for providing personalized services to teachers, since they could feed recommendations of LOs that either attract a large number of tags (‘popular’ Los) or have been tagged by peers with similar past tagging behavior (Klašnja-Milićević et al., 2015).
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LORs developers can develop appropriate tagging interfaces, in order to facilitate the anticipated use of a social tagging system. For example, by providing users with access (during the tagging process) to the popular tags of the system, as well as to the popular tags for a specific LO could facilitate reuse.