Ketidaklaziman Kolokasi Pembelajar Bipa dan Implikasinya terhadap Pembelajaran Bahasa

Unacceptable Collocations by Learners of Indonesian as a ForeignLanguage and the Implication in Language Learning. Foreign language learners\u27ability to collocate words that are natural and acceptable in the target language isimportant in foreign language learning; however, it is notoriously difficult forforeign language learners and sometimes makes them frustrated. This studyattempts to describe the negative transfer of English collocations into Indonesiancollocations made by learners of Indonesian as a foreign language in their writingassignments. This study employed a qualitative descriptive method. The data werecollected from 36 writing assignments by 12 learners whose mother tongue isEnglish. They were trainee teachers with experience in teaching Indonesian inAustralia. The finding shows that there are 176 unnatural Indonesian collocations,some of which are negative transfers of learners\u27 mother tongue. This suggests thatdirect teaching of collocations should be given special emphasis in teachingIndonesian as a foreign language

Uncovering Spatiotemporal Characteristics of Human Online Behaviors during Extreme Events

<div><p>In response to an extreme event, individuals on social media demonstrate interesting behaviors depending on their backgrounds. By making use of the large-scale datasets of posts and search queries collected from Twitter and GoogleTrends, we first identify the distinct categories of human collective online concerns and durations based on the distributions of solo tweets and new incremental tweets about events. Such a characterization enables us to gain a better understanding of dynamic changes in human behaviors corresponding to different types of events. Next, we observe the heterogeneity of individual responses to events through measuring the fraction of event-related tweets relative to the tweets released by an individual, and thus empirically confirm the heterogeneity assumption as adopted in the meta-population models for characterizing collective responses to events. Finally, based on the correlations of information entropy in different regions, we show that the observed distinct responses may be caused by their different speeds in information propagation. In addition, based on the detrended fluctuation analysis, we find that there exists a self-similar evolution process for the collective responses within a region. These findings have provided a detailed account for the nature of distinct human behaviors on social media in presence of extreme events.</p></div

The illustration of classification in the <i>CD</i> space based on the ranking values of human concerns and durations (<i>k</i> = 4 in KM algorithm).

<p>Some events, as shown in (c) and (e), are categorized into groups even if they have similar shapes of time series. Through comparing the temporal dynamic of collective online behaviors from Twitter, we find that our method can overcome the effect of multiple peaks in the curve, and provide more precise classification even if two events have similar shape of time series data.</p

Heterogenous regional responses measured by the sensitive people.

<p>(a) The positive relationship between the percentage of sensitive people and the total amount of active people who has posted a tweet about a certain event at least once. (b) The nonlinear relationship between the percentage of sensitive people and the number of different <math><msubsup><mi>C</mi><mi>i</mi><mi>p</mi></msubsup></math> in all regions. The more people take part in the discussions about an event, the more heterogeneity of people will appear that cause the diversity of human responses as shown in Fig H in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138673#pone.0138673.s001" target="_blank">S1 Appendix</a>.</p

Statistical results of collective behaviors.

<p>(a) The regularity of active users and their tweets, i.e., the distribution of <i>N</i>^<i>p</i> of people. (b)(c) The regularity of active users about ‘#nuclear’, i.e., the distribution of <math><msubsup><mi>N</mi><mrow><mo>#</mo><mi>n</mi><mi>u</mi><mi>c</mi><mi>l</mi><mi>e</mi><mi>a</mi><mi>r</mi></mrow><mi>p</mi></msubsup></math> and <math><msubsup><mi>C</mi><mrow><mo>#</mo><mi>n</mi><mi>u</mi><mi>c</mi><mi>l</mi><mi>e</mi><mi>a</mi><mi>r</mi></mrow><mi>p</mi></msubsup></math> of people, respectively. (d) The distribution of heterogeneous groups and the number of people in each group based on the same concern about ‘#nuclear’. A point (<i>x</i>, <i>y</i>) in (d) indicates that there is <i>y</i> different groups and each group have <i>x</i> people with the same <i>C</i>^<i>p</i>. (e) The distribution of <i>C</i>^<i>p</i> where <i>y</i> = 1 in (d), which covers almost 81% of the total people in (c). More specifically, each dot in (e) is aggregated over all people in different regions as shown in (f). (f) The self-similarity distribution of <i>C</i>^<i>p</i> (where <i>y</i> = 1) on the nuclear crisis in different regions. There exist an internal consistency of the distribution of concerns about an event among different regions. Through extending hashtag ‘#nuclear’ to term ‘nuclear’, such a nonlinear characteristic is becoming more evident as shown in Fig G in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138673#pone.0138673.s001" target="_blank">S1 Appendix</a>.</p

The dynamic change of regional information propagation.

<p>Generally speaking, the regional heterogeneity of information propagation is stable as shown in (a). However, there is a large fluctuation among regional information propagation in presence of different events as shown in (c)(d).</p

Distributions of collective online behaviors.

<p>(a) the cumulative distribution of solo tweets about two hashtags and their exponents <i>α</i>_<i>c</i>, and (b) the corresponding distribution of incremental tweets about two hashtags and their exponents <i>α</i>_<i>t</i>. All exponents are estimated by applying KS statistical test.</p

The correlation among information spreading in different regions.

<p>There exist a heterogeneous regional responses to events based on the types of events and regional profiles.</p

The results of collective time series measured by DFA in a log-log plot.

<p>The symbols represent the different regional responses to the same event. Similar scaling behaviors indicate that human collective online behaviors as a show follow a long-correlated self-similar process. Especially, such a long-range correlation is changed with time granularity. For example, the time series present long-range correlation by the hour regardless of regional profiles and the types of events. Yet the time series measured by the day, as shown in Fig K in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138673#pone.0138673.s001" target="_blank">S1 Appendix</a>, may not be correlated based on the type of events.</p

Comparison and validation of the results calculated by the TruckSim model and the developed model.

Comparison and validation of the results calculated by the TruckSim model and the developed model.</p