Pursuing an Export Culture Through the Teaching of Asian Languages in Australian Schools - the Gap between Theory, Practice and Policy Prescription

In February 1994, the Coalition of Australian Governments (COAG) endorsed a report it commissioned in December 1992 on a policy prescription for the study of Asian Languages and Cultures in Australian schools. The acceptance of this report, Asian Languages and Australia's Economic Future (1994), referred to as the Rudd Report after the Chair of the Working Group, was significant. It offered a 15-year plan that aimed to produce an Asia-literate generation fluent and familiar with "export" Asian languages and cultures. In particular, students would have the opportunity to commence the study of one of four priority "export" Asian languages, namely, Korean, Japanese, Indonesian, and Chinese, in primary school. However, the Rudd Report’s emphasis on prioritising Asian languages for utilitarian reasons was opposed by those who advocated the study of European languages. This paper examines some of the assumptions about second language acquisition that the Rudd Report made and argues that greater emphasis should have been placed on addressing those theoretical and pedagogical issues significant to LOTE teaching in Australia

A Hybrid Recommender Strategy on an Expanded Content Manager in Formal Learning

The main topic of this paper is to find ways to improve learning in a formal Higher Education Area. In this environment, the teacher publishes or suggests contents that support learners in a given course, as supplement of classroom training. Generally, these materials are pre-stored and not changeable. These contents are typically published in learning management systems (the Moodle platform emerges as one of the main choices) or in sites created and maintained on the web by teachers themselves. These scenarios typically include a specific group of students (class) and a given period of time (semester or school year). Contents reutilization often needs replication and its update requires new edition and new submission by teachers. Normally, these systems do not allow learners to add new materials, or to edit existing ones. The paper presents our motivations, and some related concepts and works. We describe the concepts of sequencing and navigation in adaptive learning systems, followed by a short presentation of some of these systems. We then discuss the effects of social interaction on the learners’ choices. Finally, we refer some more related recommender systems and their applicability in supporting learning. One central idea from our proposal is that we believe that students with the same goals and with similar formal study time can benefit from contents' assessments made by learners that already have completed the same courses and have studied the same contents. We present a model for personalized recommendation of learning activities to learners in a formal learning context that considers two systems. In the extended content management system, learners can add new materials, select materials from teachers and from other learners, evaluate and define the time spent studying them. Based on learner profiles and a hybrid recommendation strategy, combining conditional and collaborative filtering, our second system will predict learning activities scores and offers adaptive and suitable sequencing learning contents to learners. We propose that similarities between learners can be based on their evaluation interests and their recent learning history. The recommender support subsystem aims to assist learners at each step suggesting one suitable ordered list of LOs, by decreasing order of relevance. The proposed model has been implemented in the Moodle Learning Management System (LMS), and we present the system’s architecture and design. We will evaluate it in a real higher education formal course and we intend to present experimental results in the near future

Biterm Topic Modelを用いたeラーニングコースのレポート分類

近年、Computer Supported Collaborative Learning（CSCL）システムが開発されている。CSCLはコンピュータ技術を利用して、学習コミュニティの中での知識の共有と建設を特徴としている。しかし、CSCLは同時に同一トピックを学習するメンバによって構成される学習コミュニティを支援するので，メンバの熟達レベルの多様性が小さく，他者から学び方や学習成果を共有できる範囲は限定される。この制限を克服するために、eポートフォリオシステムは提案されている。E-ポートフォリオシステムは長年にわたって学習者の成果や情報を収集することができる。これらのデータから有用な情報を見つけて、他の学習者を助けるために、トピックモデルが適用されているeポートフォリオシステムが提案されている。　　トピックモデルは、ドキュメントのコレクションで発生する抽象的な「トピック」を発見するための統計モデルの一種である。Latent Dirichlet Allocation（LDA）は、eポートフォリオに適用することが提案されている。しかし、LDA はデータがスパースな場合、推定精度が落ちるなどの問題がある。まず、短い文書では、ほとんどの単語が一度だけしか出現しない。つまり、単語の出現頻度から、重要な単語を識別なことが困難である。第二に、多くの単語の意味は、その単語が出現する文脈によって決定される。短い文章では、関連する単語の数によって制限されてきたので、それが曖昧な単語のトピックを識別することは困難である。こんなデータのスパースは、伝統的なトピックモデルの推定精度に影響を与える。この問題に対処するために、Biterm Topic Model（BTM）が提案されている。本研究では、文書分類のための代わりにLDAのBTMを使用するように触発されている。 　 BTMのパフォーマンスを測定するために、本研究は、eラーニングシステム"samurai"に蓄積されている学習者レポートを用いた。実験の結果は、1）BTMはLDAより推定したトピックを構成する単語の一貫性が高い。2）BTMはLDAよりトピックの推定精度が高い。電気通信大学201

Towards a competency model for adaptive assessment to support lifelong learning

Adaptive assessment provides efficient and personalised routes to establishing the proficiencies of learners. We can envisage a future in which learners are able to maintain and expose their competency profile to multiple services, throughout their life, which will use the competency information in the model to personalise assessment. Current competency standards tend to over simplify the representation of competency and the knowledge domain. This paper presents a competency model for evaluating learned capability by considering achieved competencies to support adaptive assessment for lifelong learning. This model provides a multidimensional view of competencies and provides for interoperability between systems as the learner progresses through life. The proposed competency model is being developed and implemented in the JISC-funded Placement Learning and Assessment Toolkit (mPLAT) project at the University of Southampton. This project which takes a Service-Oriented approach will contribute to the JISC community by adding mobile assessment tools to the E-framework

On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

Exploring the Influence of Gamified Digital Learning on Student Engagement and Learning: A Case Study on Using Interactive Comics to Study Pancasila

This study investigates the engagement of students studying Pancasila (i.e., Indonesian philosophy) through interactive comics, with consideration of their backgrounds and reading habits. The data were collected through a survey delivered via Google Forms. The subjects were first-year students at Petra Christian University (PCU) studying Pancasila through interactive comics. The data were explored using descriptive statistics, hypothesis testing, and machine learning. We found that 72.55 percent of the respondents understood the material delivered through the interactive comics in detail. In addition, they described the method as fun. The hypothesis testing showed that the students were able to study Pancasila through interactive comics successfully regardless of their background (e.g., gender, GPA, living situation, major), reading preferences, and average reading duration. However, students� majors influenced the opinion that interactive comics led to a more interesting, up-to-date, and fun learning experience. Students who generally like e-books preferred learning Pancasila through interactive comics over conventional methods, and students who like reading novels concluded that learning Pancasila through interactive comics helped them study. Ultimately, 62.75 percent of the participants recommended exploring Pancasila through interactive comics. Based on the data, we can recommend using this approach