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12<br />

Part-time vs full-time enrolment. Data from the UNESCO/<br />

OECD/Eurostat (UOE) database on education show that<br />

about one in five tertiary education students study<br />

part time. In countries including Argentina, Finl<strong>and</strong>,<br />

New Zeal<strong>and</strong>, Pol<strong>and</strong> <strong>and</strong> the United States, at least<br />

one in three study part time. In many countries, parttime<br />

tertiary education courses are less regulated by<br />

the state <strong>and</strong> more likely to be provided by the private<br />

independent sector. In Japan, almost all part-time<br />

students are in the private independent sector; in<br />

Latvia <strong>and</strong> the Netherl<strong>and</strong>s, almost half are. In Albania,<br />

Denmark <strong>and</strong> Irel<strong>and</strong>, part-time students are likely to<br />

pay higher tuition fees than students in full-time studies<br />

(European Commission/EACEA/Eurydice, 2015b).<br />

Distance vs on-campus learning. Distance learning, being<br />

less tied to the time, place <strong>and</strong> pace of a campus, could<br />

make access to tertiary education more equitable <strong>and</strong><br />

affordable. However, so far, it seems its biggest effect<br />

in high income countries is enabling students to gain<br />

credits for learning modules before or parallel to their<br />

on-campus studies (Lokken <strong>and</strong> Mullins, 2014). This is a<br />

more modest result than the expectations surrounding<br />

massive online open courses (Barber et al., 2013).<br />

BOX 12.1<br />

In Colombia, a strong management information system provides policy-specific data on tertiary education<br />

Colombia has a comprehensive system of publicly available tertiary education data based on detailed regulations. The Ministry of Education works closely with campuses to<br />

communicate updates to definitions <strong>and</strong> to resolve technological issues. In turn, campuses invest in staff professional development to ensure timely submission <strong>and</strong> meet<br />

the latest data quality st<strong>and</strong>ards.<br />

There are five distinct data sets. The Higher Education Information System (SNIES) includes base indicators on academic programmes, students, professors, campus<br />

administration, research, continuing education, internationalization <strong>and</strong> infrastructure. SPADIES tracks enrolment patterns in order to monitor <strong>and</strong> prevent university<br />

dropout, with a special focus on disadvantaged students. The Colombian Institute for the Promotion of Higher Education (ICFES) records data on Saber Pro, a national<br />

examination designed to assess student learning at the end of the first university degree. The Labor Observatory for Education (OLE) tracks students into the labour market,<br />

providing information on graduate work status, salaries, <strong>and</strong> shifts in workforce dem<strong>and</strong>s linked to student history. The International Network of Information <strong>and</strong> Knowledge<br />

Sources for Science, Technology <strong>and</strong> Innovation Management (SCIENTI) monitors detailed information about academic research <strong>and</strong> development, doctoral research<br />

programmes <strong>and</strong> research institutes.<br />

Tertiary education enrolment increased rapidly between 2000 <strong>and</strong> 2015. However, the expansion affected the student intake. The percentage of newly admitted students who<br />

had scored at the lowest level in the secondary school exit examination increased from 25% in 2000 to 37% in 2010 (Figure 12.7a). The low scoring students are more likely to<br />

drop out before finishing a degree. After eight semesters, 55% had dropped out, compared with 35% of the high scoring students (Figure 12.7b).<br />

FIGURE 12.7:<br />

In Colombia, students with lower admission scores are less likely to graduate<br />

100<br />

a. Distribution of tertiary education students, by admission score,<br />

2000–2015<br />

70<br />

b. Tertiary education cumulative early leaving rate, by admission score<br />

<strong>and</strong> by semester, students who entered at the 2010 autumn semester<br />

Share of student admission (%)<br />

80<br />

60<br />

40<br />

20<br />

High<br />

Middle<br />

Low<br />

Early leaving rate (%)<br />

60<br />

50<br />

40<br />

30<br />

20<br />

10<br />

Low<br />

Middle<br />

High<br />

0<br />

2000<br />

2001<br />

2002<br />

2003<br />

2004<br />

2005<br />

2006<br />

2007<br />

2008<br />

2009<br />

2010<br />

2011<br />

2012<br />

2013<br />

2014<br />

2015<br />

0<br />

1<br />

2 3 4 5 6 7 8 9 10 11 12<br />

Semester<br />

Source: Colombia Ministry of Education (2016a, 2016b).<br />

232<br />

CHAPTER 12 | TARGET 4.3 – TECHNICAL, VOCATIONAL, TERTIARY AND ADULT EDUCATION

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