Such findings could not be easily achieved using closed questions due to the necessity of a long list of options. Despite the many advantages of open-ended questions, the manual analysis of free-text responses requires a considerable expenditure of time and money. As a result, most studies analyze only a small amount of data (in common with other qualitative research). For example, [PERSON] et al. 's study [21] on exploring students' dissatisfaction with assessment feedback due to recruitment difficulties featured just 44 participants at a college of 5000 undergraduates; [PERSON] et al. [22] applied social identity theory to their institutional NSS free-text data, processing just 60 responses in total; and, due to time and cost constraints, [PERSON], [PERSON], and [PERSON] [20] analyzed only 8% of a total of 10,000 free-text responses in their NSS study. [PERSON] et al. [23] processed more than a thousand text comments in their study of the coherence of NSS questions' ratings; although this sample was bigger, the work included only categorization, not in-depth qualitative text analysis. Conversely, this study aims to use text mining to demonstrate a way to analyze a large number of qualitative data (e.g., students' free-text responses) without vastly increased resources (i.e., human effort and time). Our contributions are as follows: At first, the study proposes a novel automatic analysis framework that can be used to automatically mine the text responses to open-ended questions in student feedback questionnaires. It aims at improving teaching practices and fostering positive learning experiences in higher education through the adoption of machine learning methods. It employs advanced text mining techniques to automatically identify the important problems, issues, and recommendations in students' open feedback on a large scale. The second contribution is the applications of both supervised learning (classification)and unsupervised learning (topic modeling) to automatically analyze and interpret students' responses to the open-ended questions in the NSS. The third and foremost contribution of this paper is implications of the models on real world data as a case study--comprising of four academic years of NSS (2014-2017) at a northwest UK university. Subsequently, the identified issues were developed into teaching interventions (two at Level 5, and two at Level 6). As a result, the responses to these teaching interventions were collected via the department's end-of-module surveys. The evaluation results showed a significant improvement in student satisfaction (22% higher scores than average), demonstrating the effectiveness of our teaching interventions and the underpinning machine learning methods. Finally, we conclude that the proposed automatic analysis framework was effective in this case and could be applied in the future to substantially reduce the time and cost demanded in practice by this process (e.g., by institution policy and decision-makers). Furthermore, because the information extraction model in the free-text responses those issues and topics of use for a variety of other purposes, our automatic text analysis framework can be easily adopted to wider studies and can be used to track student feedback, influencing institutional policies. The rest of the paper has been organized as follows: Section 2 presents a detailed review of relevant studies on student evaluation methods and text mining approaches. Section 3 presents detailed methodology, followed by case study in Section 4. Finally, Sections 5 and 6 discuss implications of case study and conclusions, respectively.