The S-SOMADS instrument has 8 constructs with 58 items total. The constructs, their definitions, and the number of items in each construct are in the table below. For each item, the possible responses are a 7-point Likert scale with 1 = Strongly Disagree, 2 = Disagree, 3 = Somewhat Disagree, 4 = Neither Agree Nor Disagree, 5 = Somewhat Agree, 6 = Agree, 7 = Strongly Agree
| Construct Name | Definition | Number of Items |
|---|---|---|
| Expectancy | Student perception of their ability to perform data science tasks | 9 |
| Perception of Difficulty | How difficult the student perceives learning data science to be | 6 |
| Academic Self-Concept | Student perception about themselves in general academic settings | 9 |
| Goal Orientation | What drives the students to learn data science | 6 |
| Attainment Value | How important success in learning data science is to the student’s sense of self | 6 |
| Worth | Student perception of the benefits from learning data science | 6 |
| Interest and Enjoyment Value | The interest a student has in data science, or their enjoyment from it | 9 |
| Utility Value | How much the student values learning data science for achieving their own goals | 7 |
| VarName | Item |
|---|---|
| sd_expectancy_1 | I am able to explain results using data to others. |
| sd_expectancy_2 | I can identify appropriate data science methods for a given problem. |
| sd_expectancy_3 | I can create appropriate data visualizations. |
| sd_expectancy_4 | I know how to convert data into a usable format. |
| sd_expectancy_5 | I can identify unethical data science practices. |
| sd_expectancy_6 | I can recognize when false claims are made using data. |
| sd_expectancy_7 | I can explain how the data collection process impacts the interpretation of the results. |
| sd_expectancy_8 | I can explain why it is important to explore confounding variables before making conclusions. |
| sd_expectancy_9 | I can write code to accomplish a data science task. |
How difficult the student perceives learning data science to be
Scores closer to 7 indicate that the student perceives learning data science to be difficult.
Scores closer to 1 indicate that the student perceives learning data science to be easy.
Possible responses 1 = Strongly Disagree, 2 = Disagree, 3 = Somewhat Disagree, 4 = Neither Agree Nor Disagree, 5 = Somewhat Agree 6 = Agree 7 = Strongly Agree
| VarName | Item |
|---|---|
| sd_difficulty_1 | Learning data science is hard for me. |
| sd_difficulty_2 | I have trouble understanding data science. |
| sd_difficulty_3 | Making sense of statistical results is difficult for me. |
| sd_difficulty_4 | Explaining data science to others is difficult for me. |
| sd_difficulty_5 | It is challenging to solve a problem that requires using data science. |
| sd_difficulty_6 | I have to work hard to understand data science. |
| VarName | Item |
|---|---|
| sd_acadsc_1 | I have good time management skills. |
| sd_acadsc_2 | When I do not understand a concept or skill, I often ask for help. |
| sd_acadsc_3 | I know how to prioritize my time to accomplish goals. |
| sd_acadsc_4 | I seek out additional information when I do not understand something. |
| sd_acadsc_5 | I find myself to be an overachiever when it comes to academics. |
| sd_acadsc_6 | I know how to take good notes. |
| sd_acadsc_7 | I avoid working on things that are intimidating to me. |
| sd_acadsc_8 | I am willing to seek help when I am struggling academically (e.g., asking a tutor/professor/colleague). |
| sd_acadsc_9 | I am willing to seek help from others when I am struggling academically. |
| VarName | Item |
|---|---|
| sd_goals_1 | I can make a difference in the world using data science. |
| sd_goals_2 | If I learn data science, I will look smart. |
| sd_goals_3 | Making a new discovery from data is personally rewarding. |
| sd_goals_4 | Data science helps me better understand media and news reports that use data. |
| sd_goals_5 | Being good at data science gives me great satisfaction. |
| sd_goals_6 | I am afraid I will look incompetent if I cannot do data science. |
| VarName | Item |
|---|---|
| sd_attain_1 | I would feel good about myself if I helped a friend or peer with data science. |
| sd_attain_2 | Completing a data science project gives me a sense of satisfaction. |
| sd_attain_3 | I would feel proud if someone told me that I was good at data science. |
| sd_attain_4 | Learning data science is important to me. |
| sd_attain_5 | Understanding data science makes me feel good about myself. |
| sd_attain_6 | Doing well in data science is important to my sense of self. |
| VarName | Item |
|---|---|
| sd_worth_1 | Learning data science is worth being frustrated at times. |
| sd_worth_2 | I would rather learn any subject other than data science. |
| sd_worth_3 | Improving my data science skills is worth the effort. |
| sd_worth_4 | The time I spend learning data science is time well spent. |
| sd_worth_5 | Learning data science is worth spending money on. |
| sd_worth_6 | I have more important things to do than learning data science. |
| VarName | Item |
|---|---|
| sd_intenj_1 | I am interested in learning more about data science. |
| sd_intenj_2 | I love using data science to solve real-world problems. |
| sd_intenj_3 | I find satisfaction in completing a data science task. |
| sd_intenj_4 | I want to learn data science. |
| sd_intenj_5 | I get excited to share things I have learned from data. |
| sd_intenj_6 | I enjoy using data to answer questions. |
| sd_intenj_7 | Doing data science is a lot of fun. |
| sd_intenj_8 | Conversations about data science are stimulating. |
| sd_intenj_9 | Playing with data is exciting. |
| VarName | Item |
|---|---|
| sd_utility_1 | Data science helps me to make informed choices for myself. |
| sd_utility_2 | Being good at data science will give me financial security. |
| sd_utility_3 | Knowing data science will help me look more appealing to employers. |
| sd_utility_4 | A lack of data science knowledge will limit my career choices. |
| sd_utility_5 | I need to know data science because it will be expected of me in the future. |
| sd_utility_6 | I will rarely use data science in the future. |
| sd_utility_7 | Data science is helpful for understanding the world around me. |
When giving the S-SOMADS at the end of the semester, these questions are asked.
There are similar questions for the instructor to answer. These are in the Post-EPIC-DS.
Students are asked “Did your data science course have live meetings?
If the student answered, Yes, they received the 1 question about asking questions during class, tsr_class; else all the other TSR questions are the same.
Possible responses 1 = Strongly Disagree, 2 = Disagree, 3 = Somewhat Disagree, 4 = Neither Agree Nor Disagree, 5 = Somewhat Agree 6 = Agree 7 = Strongly Agree
| VarName | Choices |
|---|---|
| live_meet | Yes, this class had live meetings (in person and/or online) |
| live_meet | No, this class did not have live meetings (it was only asynchronous) |
| VarName | Item |
|---|---|
| tsr_class | I feel comfortable asking my instructor questions during class. |
| tsr_outside | I would feel comfortable contacting my instructor with questions outside of class. |
| tsr_care | My instructor cares about me. |
| tsr_interact | All interactions I have with my instructor are positive. |
| tsr_individual | My instructor knows me individually. |
| tsr_community | My instructor creates opportunities for students to build community in class. |
| Characteristic | Question or Description |
|---|---|
| gender | What gender do you identify with? |
| age | What is your age in years? Please enter an integer. |
| race_1 | What is your race, ethnicity, or origin? |
| first_gen | Are you a first-generation college student? (A first-generation college student is a student whose parents did not complete a four-year college degree.) |
| fluency | This course is taught in a language in which |
| class_level | Which of the following best classifies you as a student? |
| why_take_1 | Which of the following best describes why you are taking this class? |
| success_ds | Have you successfully completed a data science class before? |
| ds_where_1 | Where did you take that data science course? |
| success_stat | Have you successfully completed a statistics class before? |
| stat_where_1 | Where did you take this statistics course? |
| success_cs | Have you successfully completed a computer science class before? |
| cs_where_1 | Where did you take this computer science course? |
| expected_grade | What grade do you anticipate to receive in this course? |
| gpa_have | Do you have a college GPA? |
| gpa_college_number_1_1_1 | Please give your cumulative college GPA. For example, “My cumulative college GPA is 2.87 out of 4… - gpa_college#1 - My cumulative college GPA is - Please provide numeric values below. |
| gpa_college_number_1_2_1 | Please give your cumulative college GPA. For example, “My cumulative college GPA is 2.87 out of 4… - gpa_college#1 - out of (maximum GPA possible) - Please provide numeric values below. |
| gpa_hs_number_1_1_1 | Please give your cumulative high school GPA. For example, “My cumulative high school GPA was 2.87… - gpa_hs#1 - My cumulative high school GPA was - Please provide numeric values below. |
| gpa_hs_number_1_2_1 | Please give your cumulative high school GPA. For example, “My cumulative high school GPA was 2.87… - gpa_hs#1 - out of (maximum GPA possible) - Please provide numeric values below. |
| major1_1 | Pick the field that best describes your major or intended major. - First Major Category |
| major1_2 | Pick the field that best describes your major or intended major. - Field of Study |
| major2_1 | If you have a second major, pick the field that best describes your second major or second intended major. - Second Major Category |
| major2_2 | If you have a second major, pick the field that best describes your second major or second intended major. - Field of Study |
| minor1_1 | If you have a minor, pick the field that best describes your minor. - First Minor Category |
| minor1_2 | If you have a minor, pick the field that best describes your minor. - Field of Study |
| minor2_1 | If you have a second minor, pick the field that best describes your second minor or second intended minor. - Second Minor Category |
| minor2_2 | If you have a second minor, pick the field that best describes your second minor or second intended minor. - Field of Study |
| feedback | You are welcome to provide any other comments about your attitudes toward data science. |
| VarName | Choices |
|---|---|
| gender | Woman |
| gender | Man |
| gender | Non-binary |
| gender | Prefer not to disclose |
| gender | Prefer to self-describe: |
| gender_5_text | NA |
| VarName | Choices |
|---|---|
| age | NA |
| VarName | Choices |
|---|---|
| race_1 | White: German, Irish, English, French, etc. |
| race_2 | Black or African-American: African American, Haitian, Nigerian, etc. |
| race_3 | Hispanic, Latino or Spanish origin: Mexican, Mexican-American, Puerto Rican, Cuban, Argentinean, Dominican, Salvadoran, Spaniard, etc. |
| race_4 | Middle Eastern or North African: Lebanese, Egyptian, Turkish, Iranian, etc. |
| race_5 | American Indian or Alaskan Native: Navajo, Mayan, Tlingit, etc. |
| race_6 | Asian: Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, Hmong, Laotian, Thai, Pakistani, Cambodian, etc. |
| race_7 | Native Hawaiian or Pacific Islander: Native Hawaiian, Guamanian, Samoan, Fijian, etc. |
| race_8 | Other race or origin: Provide race(s) or origin(s) below. |
| race_9 | Prefer not to answer |
| race_8_text | Other race or origin: Provide race(s) or origin(s) below. - Text |
| VarName | Choices |
|---|---|
| first_gen | Yes |
| first_gen | No |
| first_gen | Prefer not to answer |
| VarName | Choices |
|---|---|
| fluency | I am a native speaker. |
| fluency | I am fluent, but not a native speaker. |
| fluency | I am proficient. |
| fluency | I am conversant at an intermediate level. |
| fluency | I have only basic or little knowledge. |
| fluency | I have no knowledge. |
| VarName | Choices |
|---|---|
| class_level | High school student |
| class_level | Freshman (First Year) |
| class_level | Sophomore (Second Year) |
| class_level | Junior (Third Year) |
| class_level | Senior (Fourth Year) |
| class_level | Fifth or more years in college |
| class_level | Part-time student in college |
| class_level | Graduate student |
| class_level | Other: |
| class_level_9_text | NA |
| VarName | Choices |
|---|---|
| why_take_1 | It is required for my major. |
| why_take_2 | It is an elective for my major. |
| why_take_3 | It is required for my minor. |
| why_take_4 | It is an elective for my minor. |
| why_take_5 | If fulfills a general education requirement. |
| why_take_6 | It is not part of my major or minor or general education program, but it gives me additional credits needed to graduate. |
| why_take_7 | Other: |
| why_take_7_text | Other: - Text |
| VarName | Choices |
|---|---|
| success_ds | Yes |
| success_ds | No |
| VarName | Choices |
|---|---|
| ds_where_1 | High school |
| ds_where_2 | College |
| ds_where_3 | Other |
| ds_where_3_text | Other - Text |
| VarName | Choices |
|---|---|
| success_stat | Yes |
| success_stat | No |
| VarName | Choices |
|---|---|
| stat_where_1 | High School non-Advanced Placement |
| stat_where_2 | High School Advanced Placement Statistics |
| stat_where_3 | One other college-level statistics course |
| stat_where_4 | More than one other college-level statistics course |
| VarName | Choices |
|---|---|
| success_cs | Yes |
| success_cs | No |
| VarName | Choices |
|---|---|
| cs_where_1 | High School non-Advanced Placement |
| cs_where_2 | High School Advanced Placement Computer Science A |
| cs_where_3 | High School Advanced Placement Computer Science Principles |
| cs_where_4 | One other college-level computer science course |
| cs_where_5 | More than one other college-level computer science course |
| VarName | Choices |
|---|---|
| cs_other | I have no programming experience. |
| cs_other | I have a little programming experience. |
| cs_other | I have learned one language, but do not use it frequently. |
| cs_other | I am very familiar with one programming language. |
| cs_other | I know multiple programming languages well. |
| cs_other | Other |
| cs_other_6_text | NA |
| VarName | Choices |
|---|---|
| expected_grade | A |
| expected_grade | A or B (not sure which one) |
| expected_grade | B |
| expected_grade | C |
| expected_grade | D |
| expected_grade | F |
| VarName | Choices |
|---|---|
| gpa_have | Yes |
| gpa_have | No |
| VarName | Choices |
|---|---|
| gpa_college_number_1_1_1 | NA |
| gpa_college_number_1_2_1 | NA |
| VarName | Choices |
|---|---|
| gpa_hs_number_1_1_1 | NA |
| gpa_hs_number_1_2_1 | NA |
Options to be listed later
| VarName | Choices |
|---|---|
| major1_1 | NA |
| major1_2 | NA |
| major1_other | NA |
| VarName | Choices |
|---|---|
| major2_1 | NA |
| major2_2 | NA |
| major2_other | NA |
| VarName | Choices |
|---|---|
| minor1_1 | NA |
| minor1_2 | NA |
| minor1_other | NA |
| VarName | Choices |
|---|---|
| minor2_1 | NA |
| minor2_2 | NA |
| minor2_other | NA |
| VarName | Choices |
|---|---|
| feedback | NA |