(5-Year impact factor 1.376 according to Thomson Scientific 2014 Journal Citations Report)

Special issue publication date: April 2017

Submissions of initial papers due:  15 April 2016

Objectives

Language learning is a lifelong issue in the new era. From primary school to college and workplace settings, foreign language (FL) proficiency is highly valued as a requisite feature of participating competitively in the international community. To echo the trend featured in global FL learning, multiple modalities of technology enhanced language learning (TELL) are offered to satisfy diverse needs of FL learners. The various modalities can be briefly classified based on different perspectives as below:
1. Formality: formal, informal
2. Timeliness: synchronous, asynchronous
3. Reality: real world, virtual reality, augmented reality
4. Mobility: mobile, ubiquitous, specific location
5. Openness: regular class schedule, MOOCs, open courseware

No matter which perspectives are adopted, without doubt, competency acquisition of the target language should be the main focus in FL learning. Furthermore, three components in successful FL learning are essential and cannot be omitted: learners, goals, and contexts. Obviously, the information that can assist the FL learners, educators, and researchers to meaningfully take the above-mentioned components into account simultaneously would be of critical importance to successful FL learning

The learner component includes the specific characteristics of each individual which will influence learners’ second language acquisition, including learners’ ages, learning styles, nationalities, motivation, learning goals, the experience in foreign language learning, learning behaviors, learners’ metacognition (Self-regulation, self-estimation, etc.), and available learning time. The language educators and researchers definitely need the information to help them take as many as learner variables into account for providing the FL learners with successful learning in all possible TELL modalities.

Regarding the goal component, learners and educators/researchers are the two sides of FL education, and thus they wouldn’t be discussed separately. For FL educators/researchers, the information for assisting them to decide whether the goals of pedagogical syllabus are reached is as important as that for the FL learners to be perceptual about their success/failure in FL learning. The techniques of assessment interpretation for supporting the decision making by educators/researchers and learners, in the meanwhile, are also important and definitely should be valued whichever the TELL modalities are adopted by the educators/researchers or learners.

The last component, contexts, can be viewed as all the perceived phenomena including the physical surroundings in which language happens. It can be the learning platforms/systems or environments (real or virtual) in which the FL learners receive input or produce output of the target language. Additionally, language input from the environment, including contextual and non-linguistic cues, is easy to be comprehended by an L2 learner because it is in a low stress situation. Consequently, context-based FL learning is valued and emphasized in the issue of FL education and research in recent years. However, the more authentic the learning environment is, the more difficult it is for FL educators/researchers to collect and evaluate the learning process, achievement, or the behavior of the FL learners. Consequently, a technique to real-timely provide both educators/researchers and learners with a clear explanation of FL learners’ learning log, without a doubt, would be a great help in improving the outcome of FL teaching and learning.

As described at the beginning, there are various options of the modalities which fit the FL learners’ learning needs the best. At the same time, the complete learning process of the FL learners mostly recorded, whichever the modalities are adopted, in the digital era becomes the BIG DATA. Consequently, the huge volume of data produced by the leaners are so BIG that it turns to be difficult to handle and interpret via the traditional approach to provide the learners, educators, and researchers with meaningful and critical information related to the three above-mentioned essential components in FL learning. Unfortunately, it is obvious that simply providing an overwhelming amount of information does not satisfy the needs of FL learners, educators, and researchers. In a word, developing more advanced techniques to better address FL learning is an important and urgent research issue in FL learning and teaching. Using learning analytics is considered as a common way to deal with the situations mentioned above and that is why this journal calls for this special issue.

Learning analytics refers to the technique to analyze the existing, learner-produced data for assessing academic progress, predicting future performance, giving suggestions, and spotting potential issues. Through adopting learning analytics technique, it is possible for educators and researchers to better assess FL learners’ academic progress, predict future performance, provide suggestions, and to spot potential issues. Subsequently, it can enable educators to better satisfy L2 learners’ needs, predict L2 learning behaviors and outcomes, and provide L2 learners with personalized and adaptive learning. Additionally, through data visualization, L2 learners, educators, and researchers can real-timely understand and improve learning and teaching.

To this end, this special issue aims at providing a platform for researchers to present their study efforts that may offer insights into the potential of using learning analytics to analyze language learning in different modalities and scenarios. These are open questions worth of further explorations. The submitted papers will go through a double-blind review. We invite studies that provide research results and contributions that may help develop further understanding of learning analytics and language learning and inspire future research directions.

Topics of interests include, but are not limited to:

• Big data analytics in language learning
• Learning analytics in FL learning process and behavior
• Learning analytics and social language learning
• Learning analytics framework for language learning
• Learning analytics and language education
• Monitoring, explaining, and predicting FL learners’ learning
• Language education analytics
• Learning analytics in ubiquitous FL learning
• Learning analytics in multimodal communication in virtual worlds
• Learning analytics in game-based FL learning
• Learning analytics in FL learning in schools and beyond
• Visualizations in learning analytics in FL learning
• Ethical issues of learning analytics in FL learning
• Methods for using learning analytics in FL learning
• Submission Guidelines and Other considerations

This special issue will only publish original research papers (up to 7000 words). Papers submitted must not have been published previously or under consideration for publication, though they may represent significant extensions of prior work. All submitted papers will go through a rigorous double-blind peer-review process (with at least three reviewers).

Before submission, authors should carefully read over the journal’s Author Guidelines, which are located at http://www.ifets.info/guide.php. Prospective authors should submit an electronic copy of their complete manuscript using EasyChair system at:
https://easychair.org/conferences/?conf=sietslall2016.

Timeline

Submissions of initial papers due: 15 March 2016 15 April 2016
Decisions based on the double blind review process: 20 May 2016 20 June 2016
Revised manuscripts due: 30 June 2016 30 July 2016
Feedback on revised manuscripts: 30 August 2016
Final manuscripts due by the authors: 30 September 2016
Final manuscripts sent to the publishers: 31 October 2016
Special Issue Publication: 30 April 2017

Lead Guest Editor

Dr. Yu-Ju LAN, National Taiwan Normal University, Taiwan, 162. Heping East Road Section 1, Taipei 106, Taiwan, R.O.C. Email: yujulan@gmail.com

Guest editors

Dr. Nian-Shing CHEN, National Sun Yat-sen University, Taiwan. 70 Lienhai Rd., Kaohsiung 80424, Taiwan, R.O.C. Email: nschen@mis.nsysu.edu.tw
Dr. Yao-Ting SUNG, National Taiwan Normal University, Taiwan. 162, Heping East Road Section 1, Taipei 106, Taiwan, R.O.C. Email: sungtc@ntnu.edu.tw

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