One of the primary challenges in online learning environments, is to retain
learner engagement. Several different instructional strategies are proposed
both in online and offline environments to enhance learner engagement. The
Concept Attainment Model is one such instructional strategy that focuses on
learners acquiring a deeper understanding of a concept rather than just its
dictionary definition. This is done by searching and listing the properties
used to distinguish examples from non-examples of various concepts. Our work
attempts to apply the Concept Attainment Model to build conceptual riddles, to
deploy over online learning environments. The approach involves creating
factual triples from learning resources, classifying them based on their
uniqueness to a concept into `Topic Markers' and `Common', followed by
generating riddles based on the Concept Attainment Model's format and capturing
all possible solutions to those riddles. The results obtained from the human
evaluation of riddles prove encouraging.

将概念达成模型应用于在线学习环境中，通过构建概念谜题来增强学习者的参与度，并通过人工评估结果表明，这一方法是鼓舞人心的。

自动生成谜题辅助概念实现的方法

An Approach to Automatically generating Riddles aiding Concept  Attainment

Research on automated essay scoring has become increasing important because
it serves as a method for evaluating students' written-responses at scale.
Scalable methods for scoring written responses are needed as students migrate
to online learning environments resulting in the need to evaluate large numbers
of written-response assessments. The purpose of this study is to describe and
evaluate three active learning methods than can be used to minimize the number
of essays that must be scored by human raters while still providing the data
needed to train a modern automated essay scoring system. The three active
learning methods are the uncertainty-based, the topological-based, and the
hybrid method. These three methods were used to select essays included as part
of the Automated Student Assessment Prize competition that were then classified
using a scoring model that was training with the bidirectional encoder
representations from transformer language model. All three active learning
methods produced strong results, with the topological-based method producing
the most efficient classification. Growth rate accuracy was also evaluated. The
active learning methods produced different levels of efficiency under different
sample size allocations but, overall, all three methods were highly efficient
and produced classifications that were similar to one another.

本研究描述并评估了三种主动学习方法，这些方法可以用于最小化需要人工评分者评分的论文数量，同时提供培训现代自动化论文评分系统所需的数据。这三种主动学习方法是基于不确定性的，基于拓扑的和混合方法，并使用双向编码器从转换语言模型中训练出的评分模型对包含在自动化学生评估奖中的论文进行分类。所有三种主动学习方法都高效，并且产生了彼此相似的分类结果。