Elements of Success: Supporting at-risk student resilience through learning analytics

Computers & Education - Tập 152 - Trang 103890 - 2020
J. M. Russell1, Anna Marie Smith1, Russell G. Larsen2,3
1The Office of Teaching, Learning & Technology, University of Iowa, USA
2Department of Chemistry, University of Houston, USA
3Department of Chemistry, University of Iowa USA

Tóm tắt

Từ khóa


Tài liệu tham khảo

Aljohani, 2019, An integrated framework for course adapted student learning analytics dashboard, Computers in Human Behavior, 92, 679, 10.1016/j.chb.2018.03.035

Anderson, 1989, Is bad news always bad? Cue and feedback effects on intrinsic motivation, Journal of Applied Social Psychology, 19, 449, 10.1111/j.1559-1816.1989.tb00067.x

Arnold, 2012, Course Signals at Purdue: Using learning analytics to increase student success, 267

Artino, 2012, Exploring the complex relations between achievement emotions and self-regulated learning behaviors in online learning, Internet and Higher Education, 15, 170, 10.1016/j.iheduc.2012.01.006

Atif, 2015

Baneres, 2019, An early feedback prediction system for learners at-risk within a first-year higher education course, IEEE Transactions on Learning Technologies, 12, 10.1109/TLT.2019.2912167

Bodily, 2017, Review of research on student-facing learning analytics dashboards and educational recommender systems, IEEE Transactions on Learning Technologies, 10, 405, 10.1109/TLT.2017.2740172

Cai, 2015, Developing an early-alert system to promote student visits to tutor center, Learning Assistance Review, 20, 61

Campbell, 2007, Academic analytics: A new tool for a new era, Educause Review, 42, 42

Cohen, 2017, Analysis of student activity in web-supported courses as a tool for predicting dropout, Educational Technology Research & Development, 65, 1285, 10.1007/s11423-017-9524-3

Corrin, 2014, Exploring students' interpretation of feedback delivered through learning analytics dashboards, 629

Costa, 2017, Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses, Computers in Human Behavior, 73, 247, 10.1016/j.chb.2017.01.047

Credé, 2012, Adjustment to college as measured by the student adaptation to college questionnaire: A quantitative review of its structure and relationships with correlates and consequences, Educational Psychology Review, 24, 133, 10.1007/s10648-011-9184-5

Daley, 2016, Beyond performance data: Improving student help seeking by collecting and displaying influential data in an online middle-school science curriculum, British Journal of Educational Technology, 47, 121, 10.1111/bjet.12221

Dambolena, 2000, A regression exercise: Redicting final course grades from midterm results, Problems, Resources, and Issues in Mathematics Undergraduate Studies, 10, 351

Deci, 1972, Changes in intrinsic motivation as a function of negative feedback and threats

DiFrancesca, 2016, A comparison of high and low achieving students on self-regulated learning variables, Learning and Individual Differences, 45, 228, 10.1016/j.lindif.2015.11.010

Doğanay, 2011, Comparison of the level of using metacognitive strategies during study between high achieving and low achieving prospective teachers, Educational Sciences: Theory and Practice, 11, 2036

Duckworth, 2007, Grit: Perseverance and passion for long-term goals, Journal of Personality and Social Psychology, 92, 1087, 10.1037/0022-3514.92.6.1087

Duval, 2011, Attention please!: Learning analytics for visualization and recommendation, 9

Dwyer, 2019, Impact of early alert on community college student persistence in Virginia, Community College Journal of Research and Practice, 43, 228, 10.1080/10668926.2018.1449034

Elder, 2015, Identification and support of at-risk students using a case management model, Journal of Professional Nursing, 31, 247, 10.1016/j.profnurs.2014.10.003

Hamm, 2018

Howard, 2010, What makes the difference? Children and teachers talk about resilient outcomes for children “at risk, Educational Studies, 26, 321, 10.1080/03055690050137132

Huberth, 2015, Computer-tailored student support in introductory physics, PloS One, 10, 10.1371/journal.pone.0137001

Hu, 2014, Developing early warning systems to predict students' online learning performance, Computers in Human Behavior, 36, 469, 10.1016/j.chb.2014.04.002

Hurwitz, 2018, Grade inflation and the role of standardized testing, 64

Jayaprakash, 2014, Early alert of academically at-risk students: An open source analytics initiative, Journal of Learning Analytics, 1, 6, 10.18608/jla.2014.11.3

Jensen, 2014, Midterm and first-exam grades predict final grades in biology courses, Journal of College Science Teaching, 44, 82, 10.2505/4/jcst14_044_02_82

Jensen, 2008, Students' behaviors, grades, and perceptions in an introductory biology course, The American Biology Teacher, 70, 483, 10.2307/30163330

Jones, 2016, Immortal time bias in observational studies of time-to-event outcomes, Journal of Critical Care, 36, 195, 10.1016/j.jcrc.2016.07.017

Kim, 2016, Effects of learning analytics dashboard: Analyzing the relations among dashboard utilization, satisfaction, and learning achievement, Asia Pacific Education Review, 17, 13, 10.1007/s12564-015-9403-8

Lonn, 2015, Investigating student motivation in the context of a learning analytics intervention during a summer bridge program, Computers in Human Behavior, 47, 90, 10.1016/j.chb.2014.07.013

Lu, 2018, Applying learning analytics for the early prediction of students' academic performance in blended learning, Educational Technology & Society, 21, 220

Lu, 2017, Applying learning analytics for improving students Engagement and learning outcomes in an MOOCs enabled collaborative programming course, Interactive Learning Environments, 25, 220, 10.1080/10494820.2016.1278391

Macfadyen, 2010, Mining LMS data to develop an “early warning system” for educators: A proof of concept, Computers & Education, 54, 588, 10.1016/j.compedu.2009.09.008

Martin, 2006, Academic resilience and its psychological and educational correlates: A construct validity approach, Psychology in the Schools, 43, 10.1002/pits.20149

Mathiasen, 1984, Predicting college academic achievement, College Student Journal, 18, 380

Moore, 2007, Can a “reality check” improve the academic performances of at-risk students in introductory biology courses?, Bioscene, 33, 6

Park, 2015, Development of the learning analytics dashboard to support students' learning performance, Journal of Universal Computer Science, 21, 110

Pekrun, 2006, The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice, Educational Psychology Review, 18, 315, 10.1007/s10648-006-9029-9

Pekrun, 2002, Academic emotions in students' self-regulated learning and achievement: A program o qualitative and quantitative research, Educational Psychologist, 37, 91, 10.1207/S15326985EP3702_4

Pekrun, 2017, Achievement emotions and academic performance: A longitudinal model of reciprocal effects, Child Development, 88, 1653, 10.1111/cdev.12704

Perry, 2001, Academic control and action control in the achievement of college students: A longitudinal field study, Journal of Educational Psychology, 93, 776, 10.1037/0022-0663.93.4.776

Ramanathan, 2017, Can early-assignment grades predict final grades in IT courses?, ASEE

Respondek, 2017, Perceived academic control and academic emotions predict undergraduate university student success: Examining effects on dropout intention and achievement, Frontiers in Psychology, 8, 243, 10.3389/fpsyg.2017.00243

Roberts, 2017, Give me a customizable dashboard: Personalized learning analytics dashboards in higher education, Technology, Knowledge and Learning, 22, 317, 10.1007/s10758-017-9316-1

Ruban, 2006, Patterns of self‐regulatory strategy use among low‐achieving and high‐achieving university students, Roeper Review, 28, 148, 10.1080/02783190609554354

Saqr, 2017, How learning analytics can early predict under-achieving students in a blended medical education course, Medical Teacher, 39, 757, 10.1080/0142159X.2017.1309376

Sarra, 2019, Identifying students at risk of academic failure within the educational data mining framework, Social Indicators Research, 10.1007/s11205-018-1901-8

Saul, 2014, Turning learners into effective better learners: The use of the askMe! System for learning analytics, CEUR Workshop Proceedings, 1181, 57

Stewart, 2011, Attributional retraining: Reducing the likelihood of failure, Social Psychology of Education, 14, 75, 10.1007/s11218-010-9130-2

Tampke, 2013, Developing, implementing, and assessing an early alert system, Journal of College Student Retention, 14, 523, 10.2190/CS.14.4.e

Tan, 2017, Learner dashboards a double-edged sword? Students' sense-making of a collaborative critical reading and learning analytics, Journal of Learning Analytics, 4, 117, 10.18608/jla.2017.41.7

Teasley, 2017, Student-facing dashboards: One size fits all? Technology, Knowledge, and Learning, 22, 377, 10.1007/s10758-017-9314-3

Tempella, 2015, In search for the most informative data for feedback generation: Learning analytics in a data-rich context, Computers in Human Behavior, 47, 157, 10.1016/j.chb.2014.05.038

Therneau, 2000

Valiente, 2012, Linking students' emotions and academic achievement: When and why emotions matter, Child Development Perspectives, 6, 129, 10.1111/j.1750-8606.2011.00192.x

Vallerand, 1984, On the causal effects of perceived competence on intrinsic motivation: A test of cognitive evaluation theory, Journal of Sport Psychology, 6, 94, 10.1123/jsp.6.1.94

Van Horne, 2018, Facilitating student success in introductory Chemistry with feedback in an online platform, Technology, Knowledge and Learning, 23, 21, 10.1007/s10758-017-9341-0

VanZile-Tamsen, 1999, The differential impact motivation on the self-regulated strategy use of high and low-achieving college students, Journal of College Student Development, 40, 54

Willcoxson, 2011, Beyond the first-year experience: The impact on attrition of student experiences throughout undergraduate degree studies in six diverse universities, Studies in Higher Education, 36, 1, 10.1080/03075070903581533

Zanden, 2018, Pattern of success: First-year student success in multiple domains, Studies in Higher Education