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This paper examines the influence of micro-credential learning on students' perceived employability. In addition, the study aims to explore different components that will help students to gain knowledge, enhance their careers and develop their human capital (social, cultural and scholastic capital). Hence, the study also analyzed the mediating role of human capital on the aforementioned association.

Explanatory research was conducted by utilizing a correlational research design. A questionnaire comprising of closed-ended items was utilized in the study. The data was analyzed by employing PLS-SEM technique.

Our findings stipulate that micro-credential learning is an essential component to improve students' perceived employability. The study identified that micro-credential programs have a positive relationship with students' perceived employability. Moreover, the findings that micro-credential learning significantly impacts students' human capital, i.e. cultural, social and scholastic capital. Additionally, human capital acts as a significant mediator in the relationship between micro-credential learning and students' perceived employability. Moreover, participation in micro-credential learning can ensure that students can identify diverse career directions, seek information about the labor market and educational system, attain relevant qualifications for their vocations, and develop a plan for their future.

Micro-credential programs are short and focused educational programs that offer specialized knowledge and skills in a particular area. These programs are becoming increasingly popular in the modern workforce to upskill or reskill quickly and efficiently. However, lack of empirical evidence is the ultimate gap in determining the importance of micro-credential learning; as the limited literature is unable to determine the importance of MCL on students' perceived employability. Thus, the study identifies the impact of micro-credential learning on students' perceived employability.

Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get an explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural calamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon hybrid approach using Levenshtein distance, demetaphone algorithm and dictionary mapping is proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords are further validated from the search engine results at later time for detection of events.