Enhancing instructors’ capability to assess open-response using natural language processing and learning analytics

Assessments are crucial to measuring student progress and providing constructive feedback. However, the instructors have a huge workload, which leads to the application of more superficial assessments that, sometimes, does not include the necessary questions and activities to evaluate the students adequately. For instance, it is well-known that open-ended questions and textual productions can stimulate students to develop critical thinking and knowledge construction skills, but this type of question requires much effort and time in the evaluation process. Previous works have focused on automatically scoring open-ended responses based on the similarity of the students’ answers with a reference solution provided by the instructor. This approach has its benefits and several drawbacks, such as the failure to provide quality feedback for students and the possible inclusion of negative bias in the activities assessment. To address these challenges, this paper presents a new approach that combines learning analytics and natural language processing methods to support the instructor in assessing open-ended questions. The main novelty of this paper is the replacement of the similarity analysis with a tag recommendation algorithm to automatically assign correct statements and errors already known to the responses, along with an explanation for each tag.