Impacts of Low Socio-economic Status on Educational Outcomes: A Narrative Based Analysis (2022)

Abstract: Socioeconomic status (SES) is a metric used to compare a person’s social standing based on their income, level of education, and occupation. Students from low SES backgrounds are those whose parents have low income and have limited access to the resources and opportunities they need to aid their success. Researchers have studied many issues and solutions for students with low SES, and there is a lot of research going on in many fields, especially in the social sciences. Computer science, however, has not yet as a field turned its considerable potential to addressing these inequalities. Utilizing Natural Language Processing (NLP) methods and technology, our work aims to address these disparities and ways to bridge the gap. We built a simple string matching algorithm including Latent Dirichlet Allocation (LDA) topic model and Open Information Extraction (open IE) to generate relational triples that are connected to the context of the students’ challenges, and the strategies they follow to overcome them. We manually collected 16 narratives about the experiences of low SES students in higher education from a publicly accessible internet forum (Reddit) and tested our model on them. We demonstrate that our strategy is effective (from 37.50% to 80%) in gathering contextual data about low SES students, in particular, about their difficulties while in a higher educational institution and how they improve their situation. A detailed error analysis suggests that increase of data, improvement of the LDA model, and quality of triples can help get better results from our model. For the advantage of other researchers, we make our code available.

## introduction an individual's or group's socioeconomic status is defined as their social rank or class based on * these authors contributed equally to this work. 1 code may be downloaded from https://github. com/morevolution/low-ses_nlp metrics such as educational attainment, economic status and employment @xcite . the definition, however, is not limited to the aforementioned; socioeconomic status can also be linked to factors such as a person's quality of life and the privileges that are available to some people in society as opposed to others. when discussing such topics, there is an obvious inequality that has to be called out. such inequality could manifest itself in the form of disparity in equal distribution of health services @xcite , unequal educational outcomes @xcite , resource allocation (aikens and @xcite and many more. prior work in the social sciences @xcite has repeatedly demonstrated that students of low socioeconomic status, unlike their middle or high ses peers, attain lower levels of education and lack access to opportunities and resources that help them succeed in post-secondary institutions. however, this same abundance of research is not present in computer science and related fields such as nlp. there is of course some work that has been done, but most if not all of them incorporate the use of social science based structured data such as surveys, questionnaires, and focus groups to make predictions. for instance, a path based analysis of the educational attainment of low-ses students @xcite and an analysis of stem attitudes in low-ses students using descriptive statistics, confirmatory factor analysis, ordinary least squares (ols) regression, and path analysis @xcite . their approaches were almost purely computational, but the data points they based their work on were surveys-structured data. although it might seem that way, we are not trying to denigrate work made using structured data points in any way. in fact, structured data, such as questionnaires and surveys, make the act of data analysis straight forward because less time and re-sources are allocated to extract insights and bring about meaningful results. on the other hand, setting up surveys and interviews takes time, and the volume of data is always an issue. so, the motivation of our work is twofold: (1) address the lack of research in computer science, specifically nlp, pertaining to educational outcomes as a consequence of an individual's socio-economic class, and (2) use unstructured narratives from internet forums (in our case reddit) as a basis for our analysis. to be more specific, we are identifying common patterns of struggles faced by low-ses students in higher education and how those same students attempted to resolve their shortcomings. as opposed to a close reading based approach which involves subjective analysis of certain each narrative, our whole approach is predicated on distant reading-gathering generalizable insights and patterns within text in the most objective way possible. we use genims' lda model @xcite to extract generalizable topics within our corpus. we then use subject-verb-obejct (s-v-o) triples extracted by corenlp's open information extractor @xcite to provide the necessary context behind the topic clusters identified by our lda model. for each narrative, our model produces a set of s-v-o triples that reflect the challenges of the student and solutions to them. these triples are helpful for summarizing the content of the corpus, for knowledge graph construction, in question answering systems, and many other functions in addition to providing us with insightful information. the paper is organized as follows. we start by describing prior research ( §2) on socioeconomic status in relation to educational outcomes in order to describe the motivation for our work. we then describe our corpus ( §3) and our methodology ( §4) for choosing specific data points. this is then followed by our approach in topic modelling using lda and s-v-o relation extraction. we present the results ( §5) and make the limitations ( §6) of our work clear, which leads us to discussions of future research. we conclude with our contributions ( §7). ## related work in terms of educational outcomes in the realm of post secondary education, the socioeconomic strata into which an individual grew up has a direct correlation with their final educational and career outcomes @xcite . starting off, research has revealed that prospective college students from lowincome families have restricted access to information about college @xcite . this could be information about financial aid, educational resources, and vocational development. on top of that, these same students are more likely to take on higher student loan debts that surpass the of national average @xcite . the aforementioned inequalities don't even consider the negative impacts that lack of resources and support have on the early literacy of these students @xcite , their academic achievement @xcite , psychological outcomes @xcite , and career aspirations @xcite of low-ses students before they enroll in any higher educational institutions. when they do enter these institutions, low-ses students report a different sense of belonging (ahn and davis, 2020), experience financial stress that impedes their ability to succeed both academically and in social settings @xcite , and attain dissimilar levels of education as compared to their middle or high ses counterparts @xcite . previously mentioned research is also supplemented with multiple reports that address educational outcomes of low-ses students in postsecondary education as a function of their social class. one, for example, is college board report based on prospective student profiles and survey data by @xcite . it reports that low-ses students are less likely to complete a fouryear degree once on an academic track, and are less likely to pursue further education after a bachelors. they attribute this reason to a list of disadvantages that low-ses students must confront when enrolling in higher education. other work has tackled educational outcomes and how they relate with class conditioned beliefs and social-class stereotypes. rheinschmidt and mendoza-denton (2014) conduct 4 studies on students of diverse socio-economic statuses, and they found evidence that suggests that experimentally primed student beliefs about personal characteristics such as intelligence, effort, and sense of accomplishment predicted academic achievement in a college setting as a function of class-based reaction sensitivity (rheinschmidt and mendoza-denton, 2014). croizet and claire 1998 extend the concept of steele's stereotype threat, the risk of adhering to negative stereotypes about one's group @xcite , to socio-economic backgrounds as opposed to just racial and gender groups by the manipulating the instructions of tests administered to students of diverse ses in their study. some cross field research that combines the social science and computer science also address the challenges and struggles that low-ses students face in higher educational institutions such as universities and 4-year colleges. one body of work, for example, addresses the challenges that underprivileged students, such as those from low-ses, face in integrating into post-secondary institutions even with the higher levels of reported cultural and socio-economic diversity in these institutions @xcite . it uses a mixed method approach which involves an assortativity coefficient and a mean degree constrained model to test for preferential ties associated with attributes within student groups and test if those ties were related to the social class of students. there is limited amount of prior work done on low-ses students in a purely computational manner. those we manged to find relied on structured data, such as surveys and questionnaires, for their analysis. @xcite , for instance, utilized a path based analysis model in order to investigate the long-term academic progress of students of low-ses. in this study, the ordinal variables acquired from the national educational longitudinal study database were rescaled and linearized using an optimal scaling procedure to then implement a path analysis model. another study, done by @xcite , applied expectancy-value theory (evt) on survey data from a predominantly african american student district in southeastern usa in order to investigate the negative attitudes that students have toward stem fields. their analytical approach consisted of descriptive statistics (to gain better contextual understanding of data), confirmatory factor analysis (to confirm the independent variables' component structure within the data), ordinary least squares (ols) regression (to predict the potential of the evt model and emotional cost variables), and path analysis (to understand the effects of the evt constructs and emotional cost variables). another study by @xcite uses hierarchical generalized linear modelling (hglm) to analyze variables in national survey data in order to understand the influence of institutional spending and revenue on college completion rates of low-ses students. to the best of our knowledge, there is no prior work done on low ses students in the field of nlp. ## data as mentioned prior, we demonstrate our approach on unstructured social media data from the internet forum page reddit foot_0 . we were motivated to use social media data for our preliminary work because of two broad reasons: (1) the time and human resource constraints that we were working with, and (2) the scarcity of computational research that used unstructured data points. since our topic entails the collection of sensitive and private information from students or alumni, either directly or indirectly, we anticipated that surveys and interviews would be time-consuming and challenging methods for gathering data for our research. with such constraints in mind, we decided to use reddit as our preliminary source of unstructured data narratives because its users are able to express themselves in a relatively unimpeded manner, and it provided narratives that fit our qualifications best when compared to other online-forums and social media sites. in addition, the format of narratives we collected from reddit were written in prose; this is of high importance to us since the approach we applied in our preliminary study could, with slight modification and improvement, be used for the next iteration of our work. in the process of data gathering on social media sites and online forums, our qualification for a "good data point" were as follows:(1) the narratives should have the experience of being from a low-ses student and attending higher education as a focus; (2) the narratives should be about the struggles those students faced higher educational institutions and/or how they overcome those struggles, meaning no general commentary or advice; and (3) the narratives should at least be a paragraph long (150 words). when looking for data, we found that reddit provided the most data points that fit our criteria. here are some subreddits that we chose to gather our data points from: r/askreddit, r/college, r/collegeadvice, r/science, r/psychology, r/socialwork, and r/personalfinance. at this stage of our research, we chose to manually search for posts and comments using a list of manually curated keywords that was inspired by terms from our related work section. some keywords we used are: "can't pay for school", "imposter syndrome", "college culture shock", "struggled growing up", "broken family", and "first-gen in college". we, however, came up with additional terms while searching. we collected 30 narratives written by low ses students who discuss their monetary and familial challenges. for instance, some students discuss how they were raised without parental guidance, in abusive homes, with drug addictions, and without adequate financial support. they explain how these circumstances had a negative impact on their academic performance because they were forced to turn to working night shifts or two jobs to make ends meet, among other means of supporting their education. we then filtered less relevant narratives which didn't adequately discuss the challenges faced by these low-ses students. we believe the narratives we chose represent the experiences of low ses students because the students discuss how low their household income is and how they were attempting to improve their circumstances. the final number of the stories ended up at 16, and each one has an average of 15 sentences. we updated the narratives by removing symbols and personal identifying information (pii) before running our model on them. we decided not to disclose our data in order to maintain confidentiality of the narrators. besides, we are aware that making our data public will make it difficult to secure the narrators' ability to edit or remove their narratives. ## approach our approach is based on this rationale: "if low ses students documented their post secondary education experience in these narratives, then it is safe to assume that they mentioned their struggles, what factors contributed to those struggles, and how those issues were resolved". based on this rationale, we divided our approach into three parts, lda topic modeling, s-v-o triple extraction, and string matching between the topic clusters and triples. with topic modeling, we were able to identify common struggles within the low ses student community, factors such as poverty and lack of networking that contribute to such struggles, and solutions suggested within these stories that worked to alleviate these problems. s-v-o triples helped provide the necessary context behind the conclusions made by the lda model. the relevance of data points between the s-v-o triples and topic clusters produced by the lda model were addressed by string matching. we first trained and optimized a gensim lda model on a pre-processed instance of the corpus to obtain relevant topics with improved coherence scores. simultaneously, we used corenlp's open information extractor to obtain s-v-o relation triples from the raw texts of our corpus. then, we extracted the relevant s-v-o triples by string matching between the topics and triples. ## results and discussion in order to evaluate our model, we first removed the s-v-o triples that did not include any elements related to low-ses, the issues that these students face, or solutions to those issues. then, we made some inferences by comparing the triples we obtained from our model with these filtered triples. the detailed results are shown in we showed the results of two different models, one with a coherence score of 0.44 and the other 0.46. we expected to get better results from the second model, but it turns out that our first model outperformed the second. as our corpus contains only 16 narratives, the generated triples from the narratives are less in number. therefore, with a high coherence score, our model extracted generalized topics which were not very helpful to filter contextual triples from the narratives compared to the first one. additionally, we weren't able to generate more useful topics without compromising the relevance of topic clusters because of the small number of data that our model was trained on. if we look at additionally, we notice that there are more missed triples than matched triples in model 2. the lowest matched triples are for story 8, which had a percentage of 20%. and the story with the highest missed triples is story 8, with a percentage of 80% missed triples. this model produces less additional triples compared to the first model, which 77 in total. if we look at the sample output of our model in ## error analysis and future work error analysis of the results found some issues and limitations of within our methodology. these were based on limitations of the tools and the quantity of the data we utilized in our approach. ## contribution this paper makes four contributions. first, we develop a model that can generate relational triples from narratives of the students with low ses; which are important to get the insights of the life experiences of the students, specifically their struggles and strategies to overcome those struggles. second, we make a conclusion that we can employ nlp tools and technologies to understand the unstructured narratives of the students from low ses background. third, we make our code public to the community. finally, to the best of our knowledge, there is no prior work done in nlp about low ses students, our work will pave the way for other possible nlp research in this area of study.