122:. For example, analyzing data from an LMS may reveal a relationship between the learning objects that a student accessed during the course and their final course grade. Similarly, analyzing student transcript data may reveal a relationship between a student's grade in a particular course and their decision to change their academic major. Such information provides insight into the design of learning environments, which allows students, teachers, school administrators, and educational policy makers to make informed decisions about how to interact with, provide, and manage educational resources.
254:, the best methods to deliver course information and the tools to use to engage their learners for optimal learning outcomes. In particular, the distillation of data for human judgment technique provides an opportunity for educators to benefit from EDM because it enables educators to quickly identify behavioural patterns, which can support their teaching methods during the duration of the course or to improve future courses. Educators can determine indicators that show student satisfaction and engagement of course material, and also monitor learning progress.
266:– Administrators are responsible for allocating the resources for implementation in institutions. As institutions are increasingly held responsible for student success, the administering of EDM applications are becoming more common in educational settings. Faculty and advisors are becoming more proactive in identifying and addressing at-risk students. However, it is sometimes a challenge to get the information to the decision makers to administer the application in a timely and efficient manner.
753:– Plagiarism detection is an ongoing challenge for educators and faculty whether in the classroom or online. However, due to the complexities associated with detecting and preventing digital plagiarism in particular, educational data mining tools are not currently sophisticated enough to accurately address this issue. Thus, the development of predictive capability in plagiarism-related issues should be an area of focus in future research.
134:, including the increase in computing power and the ability to log fine-grained data about students' use of a computer-based learning environment, have led to an increased interest in developing techniques for analyzing the large amounts of data generated in educational settings. This interest translated into a series of EDM workshops held from 2000 to 2007 as part of several international
736:– Individual privacy is a continued concern for the application of data mining tools. With free, accessible and user-friendly tools in the market, students and their families may be at risk from the information that learners provide to the learning system, in hopes to receive feedback that will benefit their future performance. As users become savvy in their understanding of
702:
another and even with the support of statistical and visualization tools, creating one simplified version of the data can be difficult. Furthermore, choosing which data to mine and analyze can also be challenging, making the initial stages very time-consuming and labor-intensive. From beginning to end, the EDM strategy and implementation requires one to uphold
237:– Learners are interested in understanding student needs and methods to improve the learner's experience and performance. For example, learners can also benefit from the discovered knowledge by using the EDM tools to suggest activities and resources that they can use based on their interactions with the
701:
Along with technological advancements are costs and challenges associated with implementing EDM applications. These include the costs to store logged data and the cost associated with hiring staff dedicated to managing data systems. Moreover, data systems may not always integrate seamlessly with one
241:
tool and insights from past or similar learners. For younger learners, educational data mining can also inform parents about their child's learning progress. It is also necessary to effectively group learners in an online environment. The challenge is using the complex data to learn and interpret
744:
of educational data mining tools need to be proactive in protecting the privacy of their users and be transparent about how and with whom the information will be used and shared. Development of EDM tools should consider protecting individual privacy while still advancing the research in this
275:
As research in the field of educational data mining has continued to grow, a myriad of data mining techniques have been applied to a variety of educational contexts. In each case, the goal is to translate raw data into meaningful information about the learning process in order to make better
87:
record data every time a learner submits a solution to a problem. They may collect the time of the submission, whether or not the solution matches the expected solution, the amount of time that has passed since the last submission, the order in which solution components were entered into the
260:– Researchers focus on the development and the evaluation of data mining techniques for effectiveness. A yearly international conference for researchers began in 2008. The wide range of topics in EDM ranges from using data mining to improve institutional effectiveness to student performance.
724:– Research in EDM may be specific to the particular educational setting and time in which the research was conducted, and as such, may not be generalizable to other institutions. Research also indicates that the field of educational data mining is concentrated in western countries and
201:– Through the various methods and applications of EDM, discovery of new and improvements to existing models is possible. Examples include illustrating the educational content to engage learners and determining optimal instructional sequences to support the student's learning style.
457:. In particular, this method is beneficial to educators in understanding usage information and effectiveness in course activities. Key applications for the distillation of data for human judgment include identifying patterns in student learning, behavior, opportunities for
759:– It is unknown how widespread the adoption of EDM is and the extent to which institutions have applied and considered implementing an EDM strategy. As such, it is unclear whether there are any barriers that prevent users from adopting EDM in their educational settings.
149:
in 2009, the
Journal of Educational Data Mining, for sharing and disseminating research results. In 2011, EDM researchers established the International Educational Data Mining Society to connect EDM researchers and continue to grow the field.
681:). The goal for contestants was to design an algorithm that, after learning from the provided data, would make the most accurate predictions from new data. The winners submitted an algorithm that utilized feature generation (a form of
643:
397:, the created model enables the analysis between new predictions and additional variables in the study. In many cases, discovery with models uses validated prediction models that have proven generalizability across contexts.
250:– Educators attempt to understand the learning process and the methods they can use to improve their teaching methods. Educators can use the applications of EDM to determine how to organize and structure the
187:, this goal can be achieved by creating student models that incorporate the learner's characteristics, including detailed information such as their knowledge, behaviours and motivation to learn. The
529:
learning environments also suggests that data mining can be useful. Data mining can be used to help provide personalized content to mobile users, despite the differences in managing content between
446:
features of data, which for educational data mining, is used to support the development of the prediction model. Classification helps expedite the development of the prediction model, tremendously.
849:
R. Baker (2010) Data Mining for
Education. In McGaw, B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition), vol. 7, pp. 112-118. Oxford, UK: Elsevier.
639:
588:
Considerable amounts of EDM work are published at the peer-reviewed
International Conference on Educational Data Mining, organized by the International Educational Data Mining Society.
404:
and contextual variables in the learning environment. Further discovery of broad and specific research questions across a wide range of contexts can also be explored using this method.
118:, and the effect of instructional strategies embedded within various learning environments. These analyses provide new information that would be difficult to discern by looking at the
728:
and subsequently, other countries and cultures may not be represented in the research and findings. Development of future models should consider applications across multiple contexts.
284:) is discovering relationships in data. This involves searching through a repository of data from an educational environment with the goal of finding consistent relationships between
514:. Data mining tools can be used to customize learning activities for each user and adapt the pace in which the student completes the course. This is in particularly beneficial for
71:
Educational data mining refers to techniques, tools, and research designed for automatically extracting meaning from large repositories of data generated by or related to people's
51:, in order to discover new insights about how people learn in the context of such settings. In doing so, EDM has contributed to theories of learning investigated by researchers in
1694:
Yu, Hsaing-Fu; Lin, Chih-Jen; Lin, Hsuan-Tien; Lin, Shou-De; Wei, Yin-Hsuan; Weng, Jui-Yu; Change, Chun-Fu; Yan, En-Syu; McKenzie, Todd; Lou, Jing-Kai; Hsieh, Hsun-Ping (2010).
673:'s KDD Cup was conducted using data from an educational setting. The data set was provided by the DataShop, and it consisted of over 1,000,000 data points from students using a
661:
published the first
Handbook of Educational Data Mining. This resource was created for those that are interested in participating in the educational data mining community.
924:
C. Romero, S. Ventura. Educational Data Mining: A Review of the State-of-the-Art. IEEE Transactions on
Systems, Man, and Cybernetics, Part C: Applications and Reviews. 40(
345:
During phases 3 and 4, data is often visualized or in some other way distilled for human judgment. A large amount of research has been conducted in best practices for
469:
A list of the primary applications of EDM is provided by
Cristobal Romero and Sebastian Ventura. In their taxonomy, the areas of EDM application are:
1695:
158:
138:. In 2008, a group of researchers established what has become an annual international research conference on EDM, the first of which took place in
1400:
1128:
510:. Moodle contains usage data that includes various activities by users such as test results, amount of readings completed and participation in
1738:
572:
114:. EDM leverages both types of data to discover meaningful information about different types of learners and how they learn, the structure of
83:, how many times they accessed it, and how many minutes the learning object was displayed on the user's computer screen. As another example,
1089:
47:). At a high level, the field seeks to develop and improve methods for exploring this data, which often has multiple levels of meaningful
670:
390:
1114:
Azarnoush, Bahareh, et al. "Toward a
Framework for Learner Segmentation." JEDM-Journal of Educational Data Mining 5.2 (2013): 102-126.
431:, data is distilled to enable humans to identify well-known patterns, which may otherwise be difficult to interpret. For example, the
876:
401:
285:
565:
offered a free online course on "Big Data in
Education" that taught how and when to use key methods for EDM. This course moved to
435:, classic to educational studies, is a pattern that clearly reflects the relationship between learning and experience over time.
88:
interface, etc. The precision of this data is such that even a fairly short session with a computer-based learning environment (
1775:
859:
G. Siemens, R.S.j.d. Baker (2012). "Learning analytics and educational data mining: Towards communication and collaboration".
1582:
1478:
1452:
1426:
1374:
153:
With the introduction of public educational data repositories in 2008, such as the
Pittsburgh Science of Learning Centre's (
544:
New EDM applications will focus on allowing non-technical users use and engage in data mining tools and activities, making
75:
activities in educational settings. Quite often, this data is extensive, fine-grained, and precise. For example, several
1770:
1608:
1556:
1530:
1348:
548:
and processing more accessible for all users of EDM. Examples include statistical and visualization tools that analyzes
428:
417:
44:
1323:
1504:
789:
84:
76:
1634:
569:
in the summer of 2015, and has continued to run on edX annually since then. A course archive is now available online.
443:
421:
293:
969:
416:
method provides. For the use of education data mining, data is distilled for human judgment for two key purposes,
276:
decisions about the design and trajectory of a learning environment. Thus, EDM generally consists of four phases:
317:
1664:
658:
638:
Many EDM papers are routinely published in related conferences, such as
Artificial Intelligence and Education,
313:
309:
997:
Baker, R.S.; Yacef, K (2009). "The state of educational data mining in 2009: A review and future visions".
784:
741:
381:
In the Discovery with Model method, a model is developed via prediction, clustering or by human reasoning
131:
99:
52:
1129:"Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief"
382:
324:
1135:
385:
and then used as a component in another analysis, namely in prediction and relationship mining. In the
1404:
1062:
Romero, Cristobal; Ventura, Sebastian (2007). "Educational data mining: A survey from 1995 to 2005".
453:
and visually appealing way in order to understand the large amounts of education data and to support
103:
1228:
1249:
365:
and relationship mining are considered universal methods across all types of data mining; however,
297:
135:
230:
There are four main users and stakeholders involved with educational data mining. These include:
1044:
1027:
Romero, Cristobal; Ventura, Sebastian (January–February 2013). "WIREs Data Mining Knowl Discov".
882:
794:
474:
346:
107:
60:
1093:
1090:"Assessing the Economic Impact of Copyright Reform in the Area of Technology-Enhanced Learning"
1706:
1203:
872:
690:
650:
576:
534:
511:
506:
Constructing courseware – EDM can be applied to course management systems such as open source
56:
400:
Key applications of this method include discovering relationships between student behaviors,
1071:
1036:
864:
799:
720:
703:
682:
362:
301:
184:
146:
139:
115:
32:
1739:"How Can Educational Data Mining and Learning Analytics Improve and Personalize Education?"
725:
674:
549:
545:
454:
305:
188:
80:
412:
Humans can make inferences about data that may be beyond the scope in which an automated
130:
While the analysis of educational data is not itself a new practice, recent advances in
737:
686:
625:
12th International Conference on Educational Data Mining] (2019) – Montréal, QC, Canada
601:
4th International Conference on Educational Data Mining (2011) – Eindhoven, Netherlands
530:
515:
432:
281:
111:
1764:
886:
677:. Six hundred teams competed for over US$ 8,000 in prize money (which was donated by
458:
173:
1048:
598:
3rd International Conference on Educational Data Mining (2010) – Pittsburgh, PA, USA
861:
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
622:
11th International Conference on Educational Data Mining] (2018) – Buffalo, NY, USA
538:
526:
40:
616:
9th International Conference on Educational Data Mining] (2016) – Raleigh, NC, USA
1298:
1273:
607:
6th International Conference on Educational Data Mining (2013) – Memphis, TN, USA
592:
1st International Conference on Educational Data Mining (2008) – Montreal, Canada
449:
The goal of this method is to summarize and present the information in a useful,
1586:
1560:
1534:
1508:
1482:
1456:
1430:
774:
635:
EDM papers are also published in the Journal of Educational Data Mining (JEDM).
501:
450:
413:
335:
328:
213:
by building and incorporating student models, the field of EDM research and the
192:
28:
1378:
1352:
1075:
631:
14th International Conference on Educational Data Mining (2021) – Paris, France
619:
10th International Conference on Educational Data Mining] (2017) – Wuhan, China
613:
8th International Conference on Educational Data Mining] (2015) – Madrid, Spain
604:
5th International Conference on Educational Data Mining (2012) – Chania, Greece
595:
2nd International Conference on Educational Data Mining (2009) – Cordoba, Spain
341:
Predictions are used to support decision-making processes and policy decisions.
1178:
804:
749:
358:
251:
238:
214:
162:
96:
36:
1612:
1327:
938:
868:
779:
654:
439:
289:
246:
48:
610:
7th International Conference on Educational Data Mining (2014) – London, UK
102:
may contain a temporally ordered list of courses taken by the student, the
1642:
628:
13th International Conference on Educational Data Mining] (2020) – Virtual
769:
678:
562:
373:
are considered more prominent approaches within educational data mining.
218:
145:
As interest in EDM continued to increase, EDM researchers established an
119:
72:
24:
824:
95:
In other cases, the data is less fine-grained. For example, a student's
732:
1040:
389:
method use, the created model's predictions are used to predict a new
900:
707:
507:
92:
30 minutes) may produce a large amount of process data for analysis.
1672:
1029:
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
292:
for identifying such relationships have been utilized, including
79:(LMSs) track information such as when each student accessed each
1127:
U.S. Department of Education, Office of Educational Technology.
161:(NCES), public data sets have made educational data mining more
154:
1163:
Romero, C.; Ventura, S.; Pechenizkiy, M.; Baker, R. S. (2010).
1122:
1120:
1696:"Feature Engineering and Classifier Ensemble for KDD Cup 2010"
566:
176:
and Kalina Yacef identified the following four goals of EDM:
1250:"Learning Analytics | Teachers College Columbia University"
552:
and their influence on learning outcomes and productivity.
211:
Advancing scientific knowledge about learning and learners
39:
to information generated from educational settings (e.g.,
461:
and labeling data for future uses in prediction models.
110:, and when the student selected or changed his or her
1733:
1731:
1729:
1727:
242:these groups through developing actionable models.
992:
990:
280:The first phase of the EDM process (not counting
63:, and the two have been compared and contrasted.
357:Of the general categories of methods mentioned,
338:about future events in the learning environment.
1022:
1020:
1018:
1016:
1014:
1012:
644:User Modeling, Adaptation, and Personalization
207:that can be achieved through learning systems.
479:Providing feedback for supporting instructors
181:Predicting students' future learning behavior
8:
1158:
1156:
334:Validated relationships are applied to make
205:Studying the effects of educational support
165:and feasible, contributing to its growth.
939:http://educationaldatamining.org/EDM2008/
159:National Center for Education Statistics
27:field concerned with the application of
999:JEDM-Journal of Educational Data Mining
963:
961:
959:
957:
955:
953:
951:
949:
947:
816:
491:Detecting undesirable student behaviors
408:Distillation of data for human judgment
371:Distillation of Data for Human Judgment
59:. The field is closely tied to that of
323:Discovered relationships must then be
199:Discovering or improving domain models
573:Teachers College, Columbia University
7:
920:
918:
916:
845:
843:
841:
1165:Handbook of educational data mining
671:Association for Computing Machinery
518:with varying levels of competency.
14:
1351:. 20 October 2011. Archived from
1092:. Industry Canada. Archived from
191:of the learner and their overall
1064:Expert Systems with Applications
195:with learning are also measured.
106:that the student earned in each
710:for all stakeholders involved.
485:Predicting student performance
1:
1303:www.educationaldatamining.org
1278:www.educationaldatamining.org
1326:. 2011-10-20. Archived from
640:Intelligent Tutoring Systems
482:Recommendations for students
85:intelligent tutoring systems
45:intelligent tutoring systems
970:"Data Mining for Education"
901:"educationaldatamining.org"
825:"EducationalDataMining.org"
790:Glossary of education terms
77:learning management systems
1792:
1076:10.1016/j.eswa.2006.04.005
318:sequential pattern mining
659:Taylor and Francis Group
1229:"Big Data in Education"
1204:"Big Data in Education"
1179:"Big Data in Education"
869:10.1145/2330601.2330661
683:representation learning
521:Planning and scheduling
497:Social network analysis
314:association rule mining
310:social network analysis
17:Educational data mining
1776:Educational psychology
975:. oxford, UK: Elsevier
941:" Retrieved 2013-09-04
785:Educational technology
579:in Learning Analytics.
226:Users and stakeholders
132:educational technology
53:educational psychology
475:visualization of data
383:knowledge engineering
377:Discovery with models
367:Discovery with Models
863:. pp. 252–254.
697:Costs and challenges
442:for the purposes of
136:research conferences
1771:Applied data mining
1407:on 29 December 2013
1254:www.tc.columbia.edu
427:For the purpose of
395:relationship mining
157:) DataShop and the
1589:on 22 January 2022
1433:on 30 January 2014
1330:on 20 October 2011
795:Learning analytics
651:Chapman & Hall
584:Publication venues
327:in order to avoid
183:– With the use of
61:learning analytics
1615:on 15 August 2021
1459:on 8 October 2014
1041:10.1002/widm.1075
928:), 601-618, 2010.
691:Bayesian networks
561:In October 2013,
512:discussion forums
494:Grouping students
393:. For the use of
57:learning sciences
1783:
1755:
1754:
1752:
1750:
1735:
1722:
1721:
1719:
1717:
1711:
1705:. Archived from
1700:
1691:
1685:
1684:
1682:
1680:
1671:. Archived from
1661:
1655:
1654:
1652:
1650:
1641:. Archived from
1631:
1625:
1624:
1622:
1620:
1611:. Archived from
1605:
1599:
1598:
1596:
1594:
1585:. Archived from
1579:
1573:
1572:
1570:
1568:
1559:. Archived from
1553:
1547:
1546:
1544:
1542:
1533:. Archived from
1527:
1521:
1520:
1518:
1516:
1511:on 30 April 2017
1507:. Archived from
1501:
1495:
1494:
1492:
1490:
1481:. Archived from
1475:
1469:
1468:
1466:
1464:
1455:. Archived from
1449:
1443:
1442:
1440:
1438:
1429:. Archived from
1423:
1417:
1416:
1414:
1412:
1403:. Archived from
1397:
1391:
1390:
1388:
1386:
1377:. Archived from
1371:
1365:
1364:
1362:
1360:
1345:
1339:
1338:
1336:
1335:
1320:
1314:
1313:
1311:
1309:
1295:
1289:
1288:
1286:
1284:
1270:
1264:
1263:
1261:
1260:
1246:
1240:
1239:
1237:
1235:
1225:
1219:
1218:
1216:
1214:
1200:
1194:
1193:
1191:
1189:
1175:
1169:
1168:
1160:
1151:
1150:
1148:
1146:
1140:
1134:. Archived from
1133:
1124:
1115:
1112:
1106:
1105:
1103:
1101:
1096:on 13 April 2014
1086:
1080:
1079:
1059:
1053:
1052:
1024:
1007:
1006:
994:
985:
984:
982:
980:
974:
965:
942:
935:
929:
922:
911:
910:
908:
907:
897:
891:
890:
856:
850:
847:
836:
835:
833:
832:
821:
800:Machine learning
721:Generalizability
525:New research on
488:Student modeling
347:visualizing data
185:student modeling
147:academic journal
140:Montreal, Quebec
116:domain knowledge
33:machine learning
1791:
1790:
1786:
1785:
1784:
1782:
1781:
1780:
1761:
1760:
1759:
1758:
1748:
1746:
1737:
1736:
1725:
1715:
1713:
1712:on 3 March 2022
1709:
1698:
1693:
1692:
1688:
1678:
1676:
1675:on 26 June 2010
1665:"PLCS DataShop"
1663:
1662:
1658:
1648:
1646:
1645:on 15 July 2010
1633:
1632:
1628:
1618:
1616:
1607:
1606:
1602:
1592:
1590:
1581:
1580:
1576:
1566:
1564:
1555:
1554:
1550:
1540:
1538:
1529:
1528:
1524:
1514:
1512:
1503:
1502:
1498:
1488:
1486:
1477:
1476:
1472:
1462:
1460:
1451:
1450:
1446:
1436:
1434:
1425:
1424:
1420:
1410:
1408:
1399:
1398:
1394:
1384:
1382:
1373:
1372:
1368:
1358:
1356:
1347:
1346:
1342:
1333:
1331:
1322:
1321:
1317:
1307:
1305:
1299:"EDM'09 - Home"
1297:
1296:
1292:
1282:
1280:
1272:
1271:
1267:
1258:
1256:
1248:
1247:
1243:
1233:
1231:
1227:
1226:
1222:
1212:
1210:
1202:
1201:
1197:
1187:
1185:
1177:
1176:
1172:
1162:
1161:
1154:
1144:
1142:
1141:on 11 June 2014
1138:
1131:
1126:
1125:
1118:
1113:
1109:
1099:
1097:
1088:
1087:
1083:
1061:
1060:
1056:
1026:
1025:
1010:
996:
995:
988:
978:
976:
972:
967:
966:
945:
936:
932:
923:
914:
905:
903:
899:
898:
894:
879:
858:
857:
853:
848:
839:
830:
828:
823:
822:
818:
813:
766:
716:
699:
675:cognitive tutor
667:
586:
558:
550:social networks
546:data collection
467:
455:decision making
410:
402:characteristics
379:
355:
353:Main approaches
306:factor analysis
273:
239:online learning
228:
189:user experience
171:
128:
81:learning object
69:
12:
11:
5:
1789:
1787:
1779:
1778:
1773:
1763:
1762:
1757:
1756:
1745:. 18 June 2013
1723:
1686:
1656:
1635:"KDD Cup 2010"
1626:
1600:
1574:
1563:on 13 May 2022
1548:
1537:on 13 May 2022
1522:
1496:
1485:on 13 May 2022
1470:
1444:
1418:
1392:
1366:
1340:
1315:
1290:
1265:
1241:
1220:
1195:
1170:
1152:
1116:
1107:
1081:
1070:(1): 135–146.
1054:
1008:
986:
943:
930:
912:
892:
877:
851:
837:
815:
814:
812:
809:
808:
807:
802:
797:
792:
787:
782:
777:
772:
765:
762:
761:
760:
754:
746:
742:administrators
738:online privacy
729:
715:
712:
698:
695:
687:random forests
666:
663:
633:
632:
629:
626:
623:
620:
617:
614:
611:
608:
605:
602:
599:
596:
593:
585:
582:
581:
580:
570:
557:
554:
531:mobile devices
523:
522:
519:
516:online courses
504:
498:
495:
492:
489:
486:
483:
480:
477:
466:
463:
433:learning curve
429:identification
422:classification
418:identification
409:
406:
378:
375:
354:
351:
343:
342:
339:
332:
321:
294:classification
282:pre-processing
272:
269:
268:
267:
264:Administrators
261:
255:
243:
227:
224:
223:
222:
208:
202:
196:
170:
167:
127:
124:
112:academic major
68:
65:
13:
10:
9:
6:
4:
3:
2:
1788:
1777:
1774:
1772:
1769:
1768:
1766:
1744:
1740:
1734:
1732:
1730:
1728:
1724:
1708:
1704:
1697:
1690:
1687:
1674:
1670:
1666:
1660:
1657:
1644:
1640:
1636:
1630:
1627:
1614:
1610:
1604:
1601:
1588:
1584:
1578:
1575:
1562:
1558:
1552:
1549:
1536:
1532:
1526:
1523:
1510:
1506:
1500:
1497:
1484:
1480:
1474:
1471:
1458:
1454:
1448:
1445:
1432:
1428:
1422:
1419:
1406:
1402:
1396:
1393:
1381:on 8 May 2013
1380:
1376:
1370:
1367:
1355:on 9 May 2021
1354:
1350:
1344:
1341:
1329:
1325:
1319:
1316:
1304:
1300:
1294:
1291:
1279:
1275:
1269:
1266:
1255:
1251:
1245:
1242:
1230:
1224:
1221:
1209:
1205:
1199:
1196:
1184:
1180:
1174:
1171:
1166:
1159:
1157:
1153:
1137:
1130:
1123:
1121:
1117:
1111:
1108:
1095:
1091:
1085:
1082:
1077:
1073:
1069:
1065:
1058:
1055:
1050:
1046:
1042:
1038:
1034:
1030:
1023:
1021:
1019:
1017:
1015:
1013:
1009:
1004:
1000:
993:
991:
987:
971:
968:Baker, Ryan.
964:
962:
960:
958:
956:
954:
952:
950:
948:
944:
940:
934:
931:
927:
921:
919:
917:
913:
902:
896:
893:
888:
884:
880:
878:9781450311113
874:
870:
866:
862:
855:
852:
846:
844:
842:
838:
826:
820:
817:
810:
806:
803:
801:
798:
796:
793:
791:
788:
786:
783:
781:
778:
776:
773:
771:
768:
767:
763:
758:
755:
752:
751:
747:
743:
739:
735:
734:
730:
727:
723:
722:
718:
717:
713:
711:
709:
705:
696:
694:
692:
688:
684:
680:
676:
672:
669:In 2010, the
664:
662:
660:
656:
652:
647:
645:
641:
636:
630:
627:
624:
621:
618:
615:
612:
609:
606:
603:
600:
597:
594:
591:
590:
589:
583:
578:
574:
571:
568:
564:
560:
559:
555:
553:
551:
547:
542:
540:
536:
533:and standard
532:
528:
520:
517:
513:
509:
505:
503:
499:
496:
493:
490:
487:
484:
481:
478:
476:
473:Analysis and
472:
471:
470:
464:
462:
460:
459:collaboration
456:
452:
447:
445:
441:
438:Data is also
436:
434:
430:
425:
423:
419:
415:
407:
405:
403:
398:
396:
392:
388:
384:
376:
374:
372:
368:
364:
360:
352:
350:
348:
340:
337:
333:
330:
326:
322:
319:
315:
311:
307:
303:
299:
295:
291:
287:
283:
279:
278:
277:
270:
265:
262:
259:
256:
253:
249:
248:
244:
240:
236:
233:
232:
231:
225:
220:
216:
212:
209:
206:
203:
200:
197:
194:
190:
186:
182:
179:
178:
177:
175:
174:Ryan S. Baker
168:
166:
164:
160:
156:
151:
148:
143:
141:
137:
133:
125:
123:
121:
117:
113:
109:
105:
101:
98:
93:
91:
86:
82:
78:
74:
66:
64:
62:
58:
54:
50:
46:
42:
38:
34:
30:
26:
22:
18:
1747:. Retrieved
1743:EdTechReview
1742:
1714:. Retrieved
1707:the original
1702:
1689:
1677:. Retrieved
1673:the original
1668:
1659:
1647:. Retrieved
1643:the original
1638:
1629:
1617:. Retrieved
1613:the original
1603:
1591:. Retrieved
1587:the original
1577:
1565:. Retrieved
1561:the original
1551:
1539:. Retrieved
1535:the original
1525:
1513:. Retrieved
1509:the original
1499:
1487:. Retrieved
1483:the original
1473:
1461:. Retrieved
1457:the original
1447:
1435:. Retrieved
1431:the original
1421:
1409:. Retrieved
1405:the original
1395:
1383:. Retrieved
1379:the original
1369:
1357:. Retrieved
1353:the original
1343:
1332:. Retrieved
1328:the original
1318:
1306:. Retrieved
1302:
1293:
1281:. Retrieved
1277:
1268:
1257:. Retrieved
1253:
1244:
1232:. Retrieved
1223:
1211:. Retrieved
1207:
1198:
1186:. Retrieved
1182:
1173:
1167:. CRC Press.
1164:
1143:. Retrieved
1136:the original
1110:
1098:. Retrieved
1094:the original
1084:
1067:
1063:
1057:
1035:(1): 12–27.
1032:
1028:
1002:
998:
977:. Retrieved
933:
925:
904:. Retrieved
895:
860:
854:
829:. Retrieved
819:
756:
748:
731:
719:
700:
668:
648:
637:
634:
587:
543:
539:web browsers
524:
502:concept maps
468:
465:Applications
448:
437:
426:
411:
399:
394:
386:
380:
370:
366:
356:
344:
274:
263:
257:
245:
234:
229:
210:
204:
198:
193:satisfaction
180:
172:
152:
144:
129:
94:
89:
70:
41:universities
20:
16:
15:
775:Data mining
500:Developing
451:interactive
444:classifying
414:data mining
336:predictions
329:overfitting
258:Researchers
29:data mining
1765:Categories
1334:2022-07-02
1259:2015-10-13
1213:13 October
1005:(1): 2017.
979:9 February
906:2020-11-14
831:2013-07-15
811:References
805:Statistics
750:Plagiarism
714:Criticisms
387:prediction
363:clustering
359:prediction
302:clustering
298:regression
290:algorithms
288:. Several
252:curriculum
215:technology
163:accessible
142:, Canada.
100:transcript
97:university
67:Definition
37:statistics
1609:"EDM2021"
1583:"EDM2020"
1557:"EDM2019"
1531:"EDM2018"
1505:"EDM2017"
1479:"EDM2016"
1453:"EDM2015"
1427:"EDM2014"
1401:"EDM2013"
1375:"EDM2012"
1349:"EDM2011"
1324:"EDM2010"
887:207196058
780:Education
655:CRC Press
649:In 2011,
575:offers a
440:distilled
325:validated
286:variables
247:Educators
49:hierarchy
1703:DataShop
1669:DataShop
1208:edXedxed
1188:30 March
1183:Coursera
1145:30 March
1049:18019486
770:Big data
764:See also
757:Adoption
726:cultures
679:Facebook
665:Contests
563:Coursera
391:variable
235:Learners
219:software
120:raw data
73:learning
55:and the
25:research
1749:9 April
1234:17 July
1100:6 April
733:Privacy
704:privacy
556:Courses
126:History
23:) is a
1716:1 July
1679:1 July
1649:1 July
1619:1 July
1593:1 July
1567:1 July
1541:1 July
1515:1 July
1489:1 July
1463:1 July
1437:1 July
1411:1 July
1385:1 July
1359:1 July
1308:1 July
1283:1 July
1274:"Home"
1047:
885:
875:
827:. 2013
745:field.
708:ethics
689:, and
642:, and
527:mobile
508:Moodle
316:, and
271:Phases
108:course
1710:(PDF)
1699:(PDF)
1139:(PDF)
1132:(PDF)
1045:S2CID
973:(PDF)
883:S2CID
221:used.
169:Goals
104:grade
1751:2014
1718:2022
1681:2022
1651:2022
1621:2022
1595:2022
1569:2022
1543:2022
1517:2022
1491:2022
1465:2022
1439:2022
1413:2022
1387:2022
1361:2022
1310:2022
1285:2022
1236:2018
1215:2015
1190:2014
1147:2014
1102:2014
981:2014
873:ISBN
706:and
537:and
420:and
369:and
217:and
155:PSLC
90:e.g.
43:and
35:and
1639:KDD
1072:doi
1037:doi
865:doi
685:),
567:edX
535:PCs
21:EDM
1767::
1741:.
1726:^
1701:.
1667:.
1637:.
1301:.
1276:.
1252:.
1206:.
1181:.
1155:^
1119:^
1068:33
1066:.
1043:.
1031:.
1011:^
1001:.
989:^
946:^
915:^
881:.
871:.
840:^
740:,
693:.
657:,
646:.
577:MS
541:.
424:.
361:,
349:.
312:,
308:,
304:,
300:,
296:,
31:,
1753:.
1720:.
1683:.
1653:.
1623:.
1597:.
1571:.
1545:.
1519:.
1493:.
1467:.
1441:.
1415:.
1389:.
1363:.
1337:.
1312:.
1287:.
1262:.
1238:.
1217:.
1192:.
1149:.
1104:.
1078:.
1074::
1051:.
1039::
1033:3
1003:1
983:.
937:"
926:6
909:.
889:.
867::
834:.
653:/
331:.
320:.
19:(
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.