Collaborative learning has grown more popular as a form of instruction in recent decades, with a significant number of studies demonstrating its benefits from many perspectives of theory and methodology. However, it has also been demonstrated that effective collaborative learning does not occur spontaneously without orchestrating collaborative learning groups according to the provision of favourable group criteria. Researchers have investigated different foundations and strategies to form such groups. However, the group criteria semantic information, which is essential for classifying groups, has not been explored. To capture the group criteria semantic information, we propose a novel Natural Language Processing (NLP) approach, namely using pre-trained word embedding. Through our approach, we could automatically form homogeneous and heterogeneous collaborative learning groups based on student’s knowledge levels expressed in assessments. Experiments utilising a dataset from a university programming course are used to assess the performance of the proposed approach.

Automated Essay Scoring (AES) automatically allocates scores to essays at scale and may help teachers reduce the heavy burden during grading activities. Recently, researchers have deployed neural-based AES approaches to improve upon the state-of-the-art AES performance. These neural-based AES methods mainly take student essays as the sole input and focus on learning the relationship between student essays and essay scores through deep neural networks. However, their only product, the predicted holistic score, is far from providing adequate pedagogical information, such as automated writing evaluation (AWE). In this work, we propose Topic-aware BERT, a new method of learning relations among scores, student essays, as well as topical information in essay instructions. Beyond improving the AES benchmark performance, Topic-aware BERT can automatically retrieve key topical sentences in student essays by probing self-attention maps in intermediate layers. We evaluate the performance of Topic-aware BERT of different variants to (i) perform AES and (ii) retrieve key topical sentences using the open dataset Automated Student Assessment Prize and a manually annotated dataset. Our experiments show that Topic-aware BERT achieves a strong AES performance compared with the previous best neural-based AES methods and demonstrates effectiveness in identifying key topical sentences in argumentative essays.

Practice and exam-style questions, as essential educational tools, contribute to educators’ effective teaching. Automatic question generation (QG) is a promising technique that can eliminate the manual effort of constructing questions and boost technology-enhanced education systems. Recently, deep neural network-based question-generation approaches have significantly improved upon state-of-the-art of question generation. Nevertheless, these approaches are often developed based on huge and non-educational datasets consisting of over 100,000 examples, which negatively affect the scalability and reliability of the educational QG systems. This study proposes a prompt-based learning QG approach that could generate questions in a data-effective way. The proposed prompt-based learning QG approach is trained and evaluated on a general dataset SQuAD, and an educational dataset SciQ. Experiment results demonstrate that our approach outperforms existing best QG models by a vast margin in data-effective scenarios and could generate high-quality educational questions with as few as 1,000 training examples.

Large language models (LLMs), such as ChatGPT, have shown remarkable performance on various natural language processing (NLP) tasks, including educational question answering (EQA). However, LLMs generate text entirely based on knowledge obtained during pre-training, which means they struggle with recent information or domain-specific knowledge bases. Moreover, only providing answers to questions posed to LLMs without any grounding materials makes it difficult for students to judge their validity.

We therefore propose a method for integrating information retrieval systems with LLMs when developing EQA systems, which in addition to improving EQA performance grounds the answers in the educational context. Our experiments show that the proposed system outperforms vanilla ChatGPT with a vast margin of 110.9%, 67.8%, and 43.3% on BLEU, ROUGE, and METEOR scores. In addition, we argue that the use of the retrieved educational context enhances the transparency and reliability of the EQA process, making it easier to determine the correctness of the answers.

Large language models (LLMs) can be augmented by interacting with external tools and knowledge bases, allowing them to overcome some of their known limitations, such as not having access to up-to-date information or struggling to solve math problems, thereby going beyond the knowledge and capabilities obtained during pre-training. Recent prompting techniques have enabled tool-augmented LLMs to combine reasoning and action to solve complex problems with the help of tools. This is essential for allowing LLMs to strategically determine the timing and nature of tool-calling actions in order to enhance their decision-making process and improve their outputs. However, the reliance of current prompting techniques on a single reasoning path or their limited ability to adjust plans within that path can adversely impact the performance of tool-augmented LLMs. In this paper, we introduce a novel prompting method, whereby an LLM agent selects and executes one among multiple candidate strategies. We assess the effectiveness of our method on three question answering datasets, on which it outperforms state-of-the-art methods like ReWOO, while also being a competitive and more cost-efficient alternative to ReAct. We also investigate the impact of selecting a reasoning trajectory from different strategy pool sizes, further highlighting the risks in only considering a single strategy.