Learning Vocabulary in Another Language: Appendixes

An online learning community enables learners to access up-to-date information via the Internet anytime-anywhere because of the ubiquity of the World Wide Web (WWW). Students can also interact with one another during the learning process. Hence, researchers want to determine whether such interaction produces learning synergy in an online learning community. In this paper, we take the Technology Acceptance Model as a foundation and extend the external variables as well as the Perceived Variables as our model and propose a number of hypotheses. A total of 436 Taiwanese senior high school students participated in this research, and the online learning community focused on learning English. The research results show that all the hypotheses are supported, which indicates that the extended variables can effectively predict whether users will adopt an online learning community. Finally, we discuss the implications of our findings for the future development of online English learning communities.

http://ants.iis.sinica.edu.tw/3BkMJ9lTeWXTSrrvNoKNFDxRm3zFwRR/16/Extending%20the%20TAM%20model%20to%20explore%20the%20factors.pdf

Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community

Corpus linguistics - investigating language structure and use

Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983–2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.

A Systematic Review on Educational Data Mining

A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms and lets users briefly describe the intended recipient (i.e., interests, eccentricities, previously successful gifts). Based on that input, the recommendation software system and method determines the meaning of the entered terms and creatively discover connections to gift recommendations from the vast array of possibilities. The user may then make a selection from these recommendations. The search/recommendation engine allows the user to find gifts through connections that are not limited to previously available information on the Internet. Thus, interests can be connected to buying behavior by relating terms to respective items.

Recommendation systems and methods using interest correlation

With the rise in the popularity of web-based education, there is a pressing need for the design of web-based systems that are domain-specific. This need is particularly acute for the domain of second language education, where generic web-based systems fall short of fulfilling the potential of the Internet for meeting the particular challenges faced by language learners and teachers. A novel interactive online environment is described which integrates the potential of computers, Internet, and linguistic analysis to address the highly specific needs of second language composition classes. The system accommodates learners, teachers, and researchers. A crucial consequence of the interactive nature of this system is that users actually create information through their use, and this information enables the system to improve with use. Specifically, the essays written by users and the comments given by teachers are archived in a searchable online database. Learners can do pinpoint searches of this data to understand their individual persistent difficulties. Teachers can do the same in order to discover these difficulties for individual learners and for a class as a whole. The architecture of the system makes possible a novel approach to corpus analysis of learner errors. Teachers' annotations of learner errors are exploited as bootstraps which can lead to the incremental uncovering of errors without the need to heavily error tag the learner corpus. Error analysis then feeds the design of online help content.

A Web-based EFL writing environment: integrating information for learners, teachers, and researchers

This paper addresses a fundamental dilemma in the design of intelligent language learning environments: the more freedom a system offers to learners in the use of the target language, the more unwieldy the data is which the learners produce and the less able the system is to support inferences about learners from that data. It is shown how in a platform where learners and teachers interact, the teachers' feedback which is archived in the system and indexed to the learners' target language production can constitute affordances that support a process of bootstrapping from raw language output to potential insights into the learners' interlanguage and gaps in their grasp of the target language. The approach is illustrated with three types of learner errors uncovered in the corpus of learner English through this bootstrapping heuristic.

Bootstrapping in a language learning environment

In this research, we used a proxy server to search for information related to the userpsilas browsed Web pages. From the records of the proxy server we constructed a profile of the userpsilas browsing habits. At the end of the userpsilas search subsystem, we will use content based concept to extract keywords to obtain the articlepsilas characteristicspsila description. From the recommendation system, the Web pages will be classified using the hierarchical grouping method, and through collaborative filtering, the recommendation Web pages will be chosen to provide further readings for students language learning.

http://tkuir.lib.tku.edu.tw:8080/dspace/retrieve/80241/Concept-Based+Pages+Recommendation+by+Using+Cluster+Algorithm.pdf

Concept-Based Pages Recommendation by Using Cluster Algorithm

Lexical chunks have in recent years become widely recognized as a crucial aspect of second language competence. We address two major sorts of challenge that chunks pose for lexicography and describe computational approaches to addressing these challenges. The first challenge is lexical knowledge discovery, that is, the need to uncover which strings of words constitute chunks worthy of learners’ attention. The second challenge is the problem of representation, that is, how such knowledge can be made accessible to learners. To address the first challenge, we propose a greedy algorithm run on 20-million words of BNC that iterates applications of word association measures on increasingly longer n-grams. This approach places priority on high recall and then attempts to isolate false positives by sorting mechanisms. To address the challenge of representation we propose embedding the algorithm in a browser-based agent as an extension of our current browser-based collocation detection tool.

https://telearn.archives-ouvertes.fr/hal-00197301/document

A Computational Approach to the Discovery and Representation of Lexical Chunks

A premise of this paper is that there are distinctive qualities of the domain of language which render learning a language uniquely suitable for a radical contextualization within digital environments. The paper thus describes and illustrates an approach to supporting foreign language learning ubiquitously in unrestricted networked environments. The two tools presented focus on vocabulary learning. The collocator tool detects and highlights collocations (such as 'prescribe medicine' or 'stiff competition') in real time on any Web page the user is viewing. The user can select any of the highlighted collocations for focused attention, activating a 'push' mechanism that provides repeated examples of the collocation over the ensuing days. Word Spider allows users to select unknown words in any Web text and it responds by finding semantically related words in its context, automatically annotating these, exploiting them as contextual clues to the meaning of the targeted unknown word.

Nai-Lung Tsao

Papers

A Web-based EFL writing environment: integrating information for learners, teachers, and researchers

Bootstrapping in a language learning environment

Concept-Based Pages Recommendation by Using Cluster Algorithm

A Computational Approach to the Discovery and Representation of Lexical Chunks

Contextualizing language learning in the digital wild: tools and a framework