I’ve (Urn)ed This: An Application and Criterion-based Evaluation of the Urnings Algorithm

There is increased interest in personalized learning and making e-learning environments more adaptable. Some e-learning systems may use an Item Response Theory (IRT)-based assessment system. An important distinction between assessment and learning contexts is that learner proficiency is expected to remain constant across an assessment, while it is expected to change over time in a learning context. Constant learner proficiency during an assessment enables conventional approaches to estimating person and item parameters using IRT. These IRT-based systems could be abandoned for alternative approaches to modeling learners and system learning content, but assessments may provide more functions than adapting learning material to students. Thus, there is the question, how can e-learning systems with IRT-based assessment components more dynamically adapt their learning content? Is there a solution that leverages IRT for adapting the learning content of the system? A promising solution is the Urnings algorithm. Like other candidate algorithms, it is computationally light, but this algorithm has mechanisms for preventing variance inflation and is suitable for e-learning contexts. It also provides a measure of uncertainty around estimates. It has been studied both through simulations and applications to e-learning systems. Results are promising; however, there has not been an application of the Urnings algorithm to an e-learning context where there are conventionally estimated person parameters to compare the algorithm estimates to. This study addresses this gap by applying the Urnings algorithm to a K–8 reading and mathematics learning platform. In data from this platform, we have person parameter estimates across academic years from an in-system diagnostic assessment. Results from this study will help industry researchers understand the feasibility of the Urnings algorithm for large e-learning systems with IRT-based assessment components

ピアアセスメントのための項目反応理論と整数計画法を用いたグループ構成最適化

In recent years, large-scale e-learning environments such as Massive Online Open Courses (MOOCs) have become increasingly popular. In such environments, peer assessment, which is mutual assessment among learners, has been used to evaluate reports and programming assignments. When the number of learners increases as in MOOCs, peer assessment is often conducted by dividing learners into multiple groups to reduce the learners’ assessment workload. In this case, however, the accuracy of peer assessment depends on the way to form groups. To solve the problem, this study proposes a group optimization method based on item response theory (IRT) and integer programming. The proposed group optimization method is formulated as an integer programming problem that maximizes the Fisher information, which is a widely used index of ability assessment accuracy in IRT. Experimental results, however, show that the proposed method cannot sufficiently improve the accuracy compared to the random group formulation. To overcome this limitation, this study introduces the concept of external raters and proposes an external rater selection method that assigns a few appropriate external raters to each learner after the groups were formed using the proposed group optimization method. In this study, an external rater is defined as a peer-rater who belongs to different groups. The proposed external rater selection method is formulated as an integer programming problem that maximizes the lower bound of the Fisher information of the estimated ability of the learners by the external raters. Experimental results using both simulated and real-world peer assessment data show that the introduction of external raters is useful to improve the accuracy sufficiently. The result also demonstrates that the proposed external rater selection method based on IRT models can significantly improve the accuracy of ability assessment than the random selection.近年，MOOCsなどの大規模型eラーニングが普及してきた．大規模な数の学習者が参加している場合には，教師が一人で学習者のレポートやプログラム課題などを評価することは難しい．大規模の学習者の評価手法の一つとして，学習者同士によるピアアセスメントが注目されている．MOOCsのように学習者数が多い場合のピアアセスメントは，評価の負担を軽減するために学習者を複数のグループに分割してグループ内のメンバ同士で行うことが多い．しかし，この場合，グループ構成の仕方によって評価結果が大きく変化してしまう問題がある．この問題を解決するために，本研究では，項目反応理論と整数計画法を用いて，グループで行うピアアセスメントの精度を最適化するグループ構成手法を提案する．具体的には，項目反応理論において学習者の能力測定精度を表すフィッシャー情報量を最大化する整数計画問題としてグループ構成問題を定式化する．実験の結果，ランダムグループ構成と比べて，提案手法はおおむね測定精度を改善したが，それは限定的な結果であることが明らかとなった．そこで，本研究ではさらに，異なるグループから数名の学習者を外部評価者として各学習者に割り当て外部評価者選択手法を提案する．シミュレーションと実データ実験により，提案手法を用いることで能力測定精度を大幅に改善できることを示す．電気通信大学201

Point cloud-based automatic assessment of 3D computer animation courseworks

Computer-supported assessment tools can bring significant benefits to both students and teachers. When integrated in traditional education workflows, they may help to reduce the time required to perform the evaluation and consolidate the perception of fairness of the overall process. When integrated within on-line intelligent tutoring systems, they could provide students with a timely feedback and support self-assessment activities. The current work presents an alternative approach (and not just a "yet-another-implementation") to the problem of automatically evaluating technical skills needed to create 3D computer animations. Although some solutions have been reported already in the literature, their applicability is partially constrained, as they require the teaching staff to define evaluation criteria that are strictly linked to the particular animation technique being assessed. Students are forced to operate in environments where they can only perform a part of the required animation steps, by using a pre-defined set of techniques and tools. To address such limitations, the proposed system exploits shape- and time-based features extracted from the 3D point clouds (i.e., the set of data points) describing animated geometries, which are independent of the particular animation techniques used. Experimental observations collected in the evaluation of course assignments in which students were asked to recreate 3D animations of deformable meshes prepared by the teaching staff showed a good correlation between automatic and manual evaluations. Obtained results confirmed the ability of the proposed approach to cope with heterogeneous evaluation tasks in which the relevant learning outcomes can be properly considered

Designing hybridization: alternative education strategies for fostering innovation in communication design for the territory

Within the broad context of design studies, Communication Design for the Territory stands as a hybrid discipline constantly interfacing with other fields of knowledge. It assumes the territorial theme as its specific dimension, aiming to generate communication systems capable of reading the stratifications of places. From an educational perspective, teaching activities are closely linked to research and can take on different levels of complexity: from the various forms of cartographic translation to the design of sophisticated transmedia digital systems. In the wake of COVID-19, this discipline has come to terms with a profoundly changed scenario in terms of limited access to the physical space and the emergence of new technologies for remote access. In this unique context, we propose a pedagogical strategy that focuses on the hybridization of communication artifacts with the aim of fostering design experimentation. As a creative tool, hybridization leads to the design of innovative systems by strategically combining the characteristics of different artifacts to achieve specific communication goals. By experimenting with these creative strategies, students are led to critically reflect on existing communication artifacts’ features and explore original designs that deliberately combine different media, contents, and communication languages in innovative ways. Through hybridization, the methods for territorial knowledge production appear more effective, effectively combining the skills and knowledge embodied in multiple subject areas. The paper presents the experience developed in the teaching laboratories of the DCxT (Communication Design for the Territory) research group of the Design Department of Politecnico di Milano. The teaching experience highlights how hybridization strategies can increase the effectiveness in learning about territorial specificities, in acquiring critical knowledge about communication systems, and in developing innovation strategies that allow to influence the evolution of traditional communication models

Concept Mapping Strategy For Academic Writing Tutorial In Open And Distant Learning Higher Institution

Universitas Terbuka (UT) an open and distant higher education institution of Indonesia conducts the in-service teacher education program. In order to complete the program, the students – mostly teachers - have to submit the final academic paper. In fact, most of the UT students have difficulty to write this academic paper. UT offers an academic writing course to solve this writing program. Most of the student view academic writing still as a difficult assignment. Most of the students view academic writing as a difficult assignment to complete. UT has to find an appropriate instructional strategy that can facilitate student to write the academic writing assignment. One of the instructional strategy that can be selected to solve the academic writing problems is concept mapping. The aim of this study is to elaborate the implementation of concept map as an instructional strategy to facilitate the open and distance learning students io complete academic writing assignments. A design based research was applied to measure the effectiveness of using concept mapping strategy in helping students to gain academic writing skills. The steps of research and development model from Borg, Gall and Gall which consist of instructional design and development phases were implemented in this study. The result of this study indicated that students were facilitated and enjoyed the process of academic writing used the concept map strategy